| 1. |
- Benkert, P., et al.
(författare)
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Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study
- 2022
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Ingår i: The Lancet Neurology. - 1474-4422 .- 1474-4465. ; 21:3, s. 246-257
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Tidskriftsartikel (refereegranskat)abstract
- Background: Serum neurofilament light chain (sNfL) is a biomarker of neuronal damage that is used not only to monitor disease activity and response to drugs and to prognosticate disease course in people with multiple sclerosis on the group level. The absence of representative reference values to correct for physiological age-dependent increases in sNfL has limited the diagnostic use of this biomarker at an individual level. We aimed to assess the applicability of sNfL for identification of people at risk for future disease activity by establishing a reference database to derive reference values corrected for age and body-mass index (BMI). Furthermore, we used the reference database to test the suitability of sNfL as an endpoint for group-level comparison of effectiveness across disease-modifying therapies. Methods: For derivation of a reference database of sNfL values, a control group was created, comprising participants with no evidence of CNS disease taking part in four cohort studies in Europe and North America. We modelled the distribution of sNfL concentrations in function of physiological age-related increase and BMI-dependent modulation, to derive percentile and Z score values from this reference database, via a generalised additive model for location, scale, and shape. We tested the reference database in participants with multiple sclerosis in the Swiss Multiple Sclerosis Cohort (SMSC). We compared the association of sNfL Z scores with clinical and MRI characteristics recorded longitudinally to ascertain their respective disease prognostic capacity. We validated these findings in an independent sample of individuals with multiple sclerosis who were followed up in the Swedish Multiple Sclerosis registry. Findings: We obtained 10 133 blood samples from 5390 people (median samples per patient 1 [IQR 1–2] in the control group). In the control group, sNfL concentrations rose exponentially with age and at a steeper increased rate after approximately 50 years of age. We obtained 7769 samples from 1313 people (median samples per person 6·0 [IQR 3·0–8·0]). In people with multiple sclerosis from the SMSC, sNfL percentiles and Z scores indicated a gradually increased risk for future acute (eg, relapse and lesion formation) and chronic (disability worsening) disease activity. A sNfL Z score above 1·5 was associated with an increased risk of future clinical or MRI disease activity in all people with multiple sclerosis (odds ratio 3·15, 95% CI 2·35–4·23; p<0·0001) and in people considered stable with no evidence of disease activity (2·66, 1·08–6·55; p=0·034). Increased Z scores outperformed absolute raw sNfL cutoff values for diagnostic accuracy. At the group level, the longitudinal course of sNfL Z score values in people with multiple sclerosis from the SMSC decreased to those seen in the control group with use of monoclonal antibodies (ie, alemtuzumab, natalizumab, ocrelizumab, and rituximab) and, to a lesser extent, oral therapies (ie, dimethyl fumarate, fingolimod, siponimod, and teriflunomide). However, longitudinal sNfL Z scores remained elevated with platform compounds (interferons and glatiramer acetate; p<0·0001 for the interaction term between treatment category and treatment duration). Results were fully supported in the validation cohort (n=4341) from the Swedish Multiple Sclerosis registry. Interpretation: The use of sNfL percentiles and Z scores allows for identification of individual people with multiple sclerosis at risk for a detrimental disease course and suboptimal therapy response beyond clinical and MRI measures, specifically in people with disease activity-free status. Additionally, sNfL might be used as an endpoint for comparing effectiveness across drug classes in pragmatic trials. Funding: Swiss National Science Foundation, Progressive Multiple Sclerosis Alliance, Biogen, Celgene, Novartis, Roche. © 2022 Elsevier Ltd
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| 4. |
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| 5. |
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| 6. |
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| 7. |
- Meier, S, et al.
(författare)
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Serum Glial Fibrillary Acidic Protein Compared With Neurofilament Light Chain as a Biomarker for Disease Progression in Multiple Sclerosis
- 2023
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Ingår i: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 80:3, s. 287-297
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Tidskriftsartikel (refereegranskat)abstract
- There is a lack of validated biomarkers for disability progression independent of relapse activity (PIRA) in multiple sclerosis (MS).ObjectiveTo determine how serum glial fibrillary acidic protein (sGFAP) and serum neurofilament light chain (sNfL) correlate with features of disease progression vs acute focal inflammation in MS and how they can prognosticate disease progression.Design, Setting, and ParticipantsData were acquired in the longitudinal Swiss MS cohort (SMSC; a consortium of tertiary referral hospitals) from January 1, 2012, to October 20, 2022. The SMSC is a prospective, multicenter study performed in 8 centers in Switzerland. For this nested study, participants had to meet the following inclusion criteria: cohort 1, patients with MS and either stable or worsening disability and similar baseline Expanded Disability Status Scale scores with no relapses during the entire follow-up; and cohort 2, all SMSC study patients who had initiated and continued B-cell–depleting treatment (ie, ocrelizumab or rituximab).ExposuresPatients received standard immunotherapies or were untreated.Main Outcomes and MeasuresIn cohort 1, sGFAP and sNfL levels were measured longitudinally using Simoa assays. Healthy control samples served as the reference. In cohort 2, sGFAP and sNfL levels were determined cross-sectionally.ResultsThis study included a total of 355 patients (103 [29.0%] in cohort 1: median [IQR] age, 42.1 [33.2-47.6] years; 73 female patients [70.9%]; and 252 [71.0%] in cohort 2: median [IQR] age, 44.3 [33.3-54.7] years; 156 female patients [61.9%]) and 259 healthy controls with a median [IQR] age of 44.3 [36.3-52.3] years and 177 female individuals (68.3%). sGFAP levels in controls increased as a function of age (1.5% per year; P &lt; .001), were inversely correlated with BMI (−1.1% per BMI unit; P = .01), and were 14.9% higher in women than in men (P = .004). In cohort 1, patients with worsening progressive MS showed 50.9% higher sGFAP levels compared with those with stable MS after additional sNfL adjustment, whereas the 25% increase of sNfL disappeared after additional sGFAP adjustment. Higher sGFAP at baseline was associated with accelerated gray matter brain volume loss (per doubling: 0.24% per year; P &lt; .001) but not white matter loss. sGFAP levels remained unchanged during disease exacerbations vs remission phases. In cohort 2, median (IQR) sGFAP z scores were higher in patients developing future confirmed disability worsening compared with those with stable disability (1.94 [0.36-2.23] vs 0.71 [−0.13 to 1.73]; P = .002); this was not significant for sNfL. However, the combined elevation of z scores of both biomarkers resulted in a 4- to 5-fold increased risk of confirmed disability worsening (hazard ratio [HR], 4.09; 95% CI, 2.04-8.18; P &lt; .001) and PIRA (HR, 4.71; 95% CI, 2.05-9.77; P &lt; .001).Conclusions and RelevanceResults of this cohort study suggest that sGFAP is a prognostic biomarker for future PIRA and revealed its complementary potential next to sNfL. sGFAP may serve as a useful biomarker for disease progression in MS in individual patient management and drug development.
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| 8. |
- Bissell, Malenka M., et al.
(författare)
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4D Flow cardiovascular magnetic resonance consensus statement : 2023 update
- 2023
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Ingår i: Journal of Cardiovascular Magnetic Resonance. - : BMC. - 1097-6647 .- 1532-429X. ; 25:1
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Forskningsöversikt (refereegranskat)abstract
- Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 ‘4D Flow CMR Consensus Statement’. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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| 9. |
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| 10. |
- Avula, Ramana R., 1993-, et al.
(författare)
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Adversarial Inference Control in Cyber-Physical Systems : A Bayesian Approach With Application to Smart Meters
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 24933-24948
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.
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| 11. |
- Avula, Ramana Reddy, et al.
(författare)
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Adversarial Inference Control in Cyber-Physical Systems : A Bayesian Approach With Application to Smart Meters
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 24933-24948
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible ’Co-LivEn’ dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.
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| 12. |
- Avula, Ramana R., 1993-, et al.
(författare)
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Design Framework for Privacy-Aware Demand-Side Management with Realistic Energy Storage Model
- 2021
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Ingår i: IEEE Transactions on Smart Grid. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3053 .- 1949-3061. ; 12:4, s. 3503-3513
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Tidskriftsartikel (refereegranskat)abstract
- Demand-side management (DSM) is a process by which the user demand patterns are modified to meet certain desired objectives. Traditionally, DSM was utility-driven, but with an increase in the integration of renewable sources and privacy-conscious consumers, it also becomes a “consumer-driven" process. Promising theoretical studies have shown that privacy can be achieved by shaping the user demand using an energy storage system (ESS). In this paper, we present a framework for utility-driven DSM while considering the user privacy and the ESS operational cost due to its energy losses and capacity degradation. We propose an ESS model using a circuit-based and data-driven approach that can be used to capture the ESS characteristics in control strategy designs. We measure privacy leakage using the Bayesian risk of a hypothesis testing adversary and present a novel recursive algorithm to compute the optimal privacy control strategy. Further, we design an energy-flow control strategy that achieves the Pareto-optimal trade-off between privacy leakage, deviation of demand from a DSM target profile, and the ESS cost. With numerical experiments using real household data and an emulated lithium-ion battery, we show that the desired level of privacy and demand shaping performance can be achieved while reducing the ESS degradation.
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| 13. |
- Avula, Ramana R., 1993-, et al.
(författare)
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On design of optimal smart meter privacy control strategy against adversarial MAP detection
- 2020
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Ingår i: Proceedings of the ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - Barcelona, Spain : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5845-5849
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Konferensbidrag (refereegranskat)abstract
- We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks.
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| 14. |
- Avula, Ramana R., 1993-, et al.
(författare)
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Optimal privacy-by-design strategy for user demand shaping in smart grids
- 2020
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Ingår i: Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- In this work, we propose an optimal privacy-by-design strategy using an energy storage system (ESS) that is capable of shaping the user demand to follow a time-varying target profile. In addition, we consider the ESS usage cost due to its energy losses and capacity degradation. We measure the privacy leakage in terms of the Bayesian risk. The proposed strategy is computed by solving a multi-objective optimization problem using the Markov decision process framework. With numerical simulations using real household consumption data and a lithium-ion battery model, we study the trade-off between the achievable Bayesian risk, the variations in the user demand from the target profile and the energy storage cost. The results show that by trading-off some privacy, the variations in the user demand can be reduced while improving the battery lifetime.
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| 15. |
- Avula, Ramana R., 1993-, et al.
(författare)
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Privacy-Enhancing Appliance Filtering For Smart Meters
- 2022
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Ingår i: International Conference on Acoustics, Speech, and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Non-intrusive load monitoring (NILM) is the process of disaggregating total electricity consumption measured by a smart meter into individual appliances’ contributions. In this paper, we present a privacy control strategy that selectively filters appliances’ consumption from the smart meter measurements to hinder NILM disaggregation performance. The privacy controller uses charging and discharging operations of an energy storage to achieve desired smart meter measurements. We model the household consumption using both additive and difference factorial hidden Markov models and design a control strategy to minimize privacy leakage measured in terms of Bayesian risk due to maximum a posteriori detection. Due to the high computational complexity of the optimal control strategy, we propose a computationally efficient sub-optimal strategy. We evaluate the proposed approaches using the ECO data set and show their privacy improvements against the Viterbi disaggregation algorithm.
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| 16. |
- Avula, Ramana Reddy, 1993-
(författare)
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Towards Realistic Smart Meter Privacy against Bayesian Inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Smart meters, now an essential component of modern power grids, allow energy providers to remotely monitor users' energy consumption in near real-time. While this technology offers numerous advantages for energy management and system efficiency, it also poses significant privacy concerns. High-resolution energy consumption data can reveal sensitive information about users' routines and activities, thus potentially jeopardizing their privacy. In particular, research has demonstrated that Bayesian inference attacks can effectively disaggregate smart meter data to deduce household appliance states and subsequently obtain sensitive user information.This thesis investigates the use of energy storage systems to protect smart meter data privacy against Bayesian inference attacks. Although several methods have been proposed in the literature that employ energy storage systems for this purpose, many rely on ideal assumptions such as lossless energy storage systems. To address this issue, a data-driven energy storage model that considers energy losses and capacity degradation has been proposed. Privacy leakage is quantified in terms of Bayesian risk, and control strategies are devised to minimize Bayesian risk while accounting for the energy storage system's operational constraints and economic implications. The findings reveal that non-idealities in energy storage systems significantly affect the privacy-preserving performance of control strategies. Moreover, incorporating degradation losses in the design of privacy-enhancing control strategies considerably improves battery life, albeit with some privacy loss.Taking into account the non-idealities of energy storage, this thesis introduces novel privacy-preserving control strategies using various adversarial models, which are classified based on their knowledge of the control system. These models include controller-aware and controller-unaware adversaries employing sequential hypothesis testing or maximum a posteriori detection. The proposed control strategies are evaluated through numerical simulations using real data and emulated energy storage systems. Additionally, the thesis provides a reference dataset of appliance power consumption, featuring detailed electrical measurements to support future smart meter privacy research. In summary, this work offers valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with potential applications extending to other sensor networks beyond smart meters.
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| 17. |
- Bao, Yicheng, et al.
(författare)
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Proof-of-Concept of Polar Codes for Biometric Identification and Authentication
- 2022
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Ingår i: 2022 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- In this work, a complete biometrics identification and authentication system considered in [1] is implemented. In the considered system, polar codes are applied and binary symmetric memoryless channels are used for noisy enrollment and observation. The fundamental limits can be achieved with sufficiently long block length for iid binary source sequence. Fingerprints are used as the biometric source and an autoencoder is designed for pre-processing so that images are compressed to nearly uniformly distributed binary sequences with similar correlation and entropy properties to iid binary sequence. The identification and authentication system with generated secret key in [1] is implemented and simulated using pre-processed fingerprints as biometric source and polar code-based design. The proposed system design approach is systematic and flexible in choosing the optimal trade-off. The results show that identification error rates become smaller with longer code length and when the successive cancellation list algorithm is applied. Thus, it is shown by these first promising experiments that polar codes can be used in real identification and authentication systems.
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| 18. |
- Cao, Phuong, et al.
(författare)
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Optimal Transmit Strategies for Gaussian MISO Wiretap Channels
- 2020
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Ingår i: IEEE Transactions on Information Forensics and Security. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1556-6013 .- 1556-6021. ; 15, s. 829-838
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings: sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. The problem is motivated by the fact that channel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are derived. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of aligned channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Last, the theoretical results are illustrated by numerical simulations.
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| 19. |
- Cervia, Giulia, et al.
(författare)
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(epsilon, n) Fixed-Length Strong Coordination Capacity
- 2021
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Ingår i: 2021 IEEE Information Theory Workshop (ITW). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the problem of synthesizing joint distributions in the finite-length regime. For a fixed block-length n and an upper bound on the distribution approximation epsilon, we prove a capacity result for fixed-length strong coordination. It is shown analytically that the rate conditions for the fixedlength regime are lower-bounded by the mutual information that appears in the asymptotical condition plus Q(-1) (epsilon) root V/n, where V is the channel dispersion, and Q(-1) is the inverse of the tail distribution function of the standard normal distribution. A full version of this paper is accessible at: https://arxiv.org/pdf/2101.06937.pdf
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| 20. |
- Cervia, Giulia, et al.
(författare)
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Remote Joint Strong Coordination and Reliable Communication
- 2020
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Konferensbidrag (refereegranskat)abstract
- We consider a three-node network, in which two agents wish to communicate over a noisy channel, while control- ling the distribution observed by a third external agent. We use strong coordination to constrain the distribution, and we provide a complete characterization of the “remote strong coordination and reliable communication” region.
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| 21. |
- Champati, Jaya Prakash, et al.
(författare)
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Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE Communications Society. - 0733-8716 .- 1558-0008. ; 39:5, s. 1365-1379
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.
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| 22. |
- Champati, Jaya Prakash, et al.
(författare)
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On the Minimum Achievable Age of Information for General Service-Time Distributions
- 2020
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Ingår i: Proceedings 39th IEEE Conference on Computer Communications, INFOCOM 2020.
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Konferensbidrag (refereegranskat)abstract
- There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previous works, we are interested in a fundamental question, what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? To address this question, we study a problem of optimizing AoI under service preemptions. Our main result is on the characterization of the minimum achievable average peak AoI (PAoI). We obtain this result by showing that a fixed-threshold policy is optimal in the set of all randomized-threshold causal policies. We use the characterization to provide necessary and sufficient conditions for the service-time distributions under which preemptions are beneficial.
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| 23. |
- Chen, Yiqi, et al.
(författare)
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On Strong Secrecy for Multiple Access Channel with States and causal CSI
- 2023
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Ingår i: 2023 IEEE International Symposium on Information Theory, ISIT 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2744-2749
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Konferensbidrag (refereegranskat)abstract
- Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states and the channel state information (CSI) is revealed to the encoders in a causal manner. An inner bound of the capacity is provided. To establish the inner bound, we investigate coding schemes incorporating wiretap coding and secret key agreement between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are studied. The first one uses backward decoding and Wyner-Ziv coding and the secret key is constructed from a lossy reproduction of the CSI. The other one is an extended version of the existing coding scheme for point-to-point wiretap channels with causal CSI. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.
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| 24. |
- Fay, Dominik, et al.
(författare)
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Private Learning Via Knowledge Transfer with High-Dimensional Targets
- 2022
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Ingår i: ICASSP 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665405409 - 9781665405416 ; , s. 3873-3877
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Konferensbidrag (refereegranskat)abstract
- Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.
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| 25. |
- Fay, Dominik, et al.
(författare)
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Private Learning via Knowledge Transfer with High-Dimensional Targets
- 2022
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Ingår i: 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3873-3877
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Konferensbidrag (refereegranskat)abstract
- Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.
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| 26. |
- Fay, Dominik
(författare)
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Towards Scalable Machine Learning with Privacy Protection
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. With the growth of machine learning models, traditional privacy methods such as data anonymization are becoming insufficient. Thus, we delve into alternative approaches, such as differential privacy.Our research addresses the following core areas in the context of scalable privacy-preserving machine learning: First, we examine the implications of data dimensionality on privacy for the application of medical image analysis. We extend the classification algorithm Private Aggregation of Teacher Ensembles (PATE) to deal with high-dimensional labels, and demonstrate that dimensionality reduction can be used to improve privacy. Second, we consider the impact of hyperparameter selection on privacy. Here, we propose a novel adaptive technique for hyperparameter selection in differentially gradient-based optimization. Third, we investigate sampling-based solutions to scale differentially private machine learning to dataset with a large number of records. We study the privacy-enhancing properties of importance sampling, highlighting that it can outperform uniform sub-sampling not only in terms of sample efficiency but also in terms of privacy.The three techniques developed in this thesis improve the scalability of machine learning while ensuring robust privacy protection, and aim to offer solutions for the effective and safe application of machine learning in large datasets.
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| 27. |
- Ghourchian, Hamid
(författare)
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On Secure and Sequential Source Coding
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Secure source coding is an important research area in recent years as it deals with the problem of transmitting sensitive information over insecure channels while protecting it from unauthorized access. This is particularly relevant in the context of modern communication systems where the data transmitted is often sensitive in nature and the threat of eavesdropping or data breaches is high. By developing efficient and secure source coding techniques, it is possible to ensure the confidentiality and integrity of the transmitted information, thereby protecting the privacy and security of the users. In addition, secure source coding also plays a critical role in various applications such as sensor networks, wireless communications, and cloud computing. In this thesis, we explore the topic of secure source coding from an information theoretic perspective and focus on two main problems. In the first problem, we have successfully characterized the entire achievable rate-distortion-equivocation region of a specific instance of a classic problem. We investigate the challenge of balancing the trade-off between the rate of data compression, the level of distortion in the compressed data, and the amount of information leaked to an eavesdropper when a private key is shared between the sender and the receiver. Specifically, we concentrate on a scenario where the decoder and eavesdropper have access to different side-informations that are correlated with the source.In the second problem, the focus is on studying secure rate-distortion coding, where data is compressed and transmitted in a block-wise, causal manner, and the decoding is done non-causally. A new concept called cumulative rate distribution functions (CRDFs) is introduced to describe the rate resources that are spent sequentially to compress the sequence, while the concept of cumulative leakage distribution functions (CLFs) is used to characterize the security constraints on the amount of information leakage. Using techniques from majorization theory, necessary and sufficient conditions are derived for the achievable CRDFs for a given independent and identically distributed (IID) source and CLF, and it was found that the concave-hull of the CRDF characterizes the optimal achievable rate distribution. It is also extended to consider the scenario where there is a wiretap channel between the encoder, decoder, and eavesdropper, and inner and outer bounds as well as a closed-solution for a specific case of wiretap channels are found.
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| 28. |
- Ghourchian, Hamid, et al.
(författare)
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Secure Block Joint Source-Channel Coding with Sequential Encoding
- 2023
-
Ingår i: 2023 IEEE International Symposium on Information Theory, ISIT 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2732-2737
-
Konferensbidrag (refereegranskat)abstract
- We extend the results of Ghourchian et al. [1] to joint source-channel coding with eavesdropping. Our work characterizes the sequential encoding process using the cumulative rate distribution functions (CRDF) and includes a security constraint using the cumulative leakage distribution functions (CLF). The information leakage is defined based on the mutual information between the source and the output of the wiretap channel to the eavesdropper. We derive inner and outer bounds on the achievable CRDF for a given source and CLF, and show that the bounds are tight when the distribution achieving the capacity of the wiretap channel is the same as the one achieving the capacity of the channel.
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| 29. |
- Ghourchian, Hamid, et al.
(författare)
-
Secure Block Source Coding With Sequential Encoding
- 2021
-
Ingår i: IEEE Journal on Selected Areas in Information Theory. - : IEEE. - 2641-8770. ; 2:1
-
Tidskriftsartikel (refereegranskat)abstract
- We introduce fundamental bounds on achievable cumulative rate distribution functions (CRDF) to characterize a sequential encoding process that ensures lossless or lossy reconstruction subject to an average distortion criterion using a non-causal decoder. The CRDF describes the rate resources spent sequentially to compress the sequence. We also include a security constraint that affects the set of achievable CRDF. The information leakage is defined sequentially based on the mutual information between the source and its compressed representation, as it evolves. To characterize the security constraints, we introduce the concept of cumulative leakage distribution functions (CLF), which determines the allowed information leakageas distributed over encoded sub-blocks. Utilizing tools from majorization theory, we derive necessary and sufficient conditions on the achievable CRDF for a given independent and identically distributed (IID) source and CLF. One primary result of this article is that the concave-hull of the CRDF characterizes the optimal achievable rate distribution.
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| 30. |
- Ghourchian, Hamid, et al.
(författare)
-
Secure Source Coding with Side-information at Decoder and Shared Key at Encoder and Decoder
- 2021
-
Ingår i: 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- We study the problem of rate-distortion-equivocation with side-information only available at the decoder when an independent private random key is shared between the sender and the receiver. The sender compresses the sequence, and the receiver reconstructs it such that the average distortion between the source and the output is limited. The equivocation is measured at an eavesdropper that intercepts the source encoded message, utilizing side-information correlated with the source and the side-information at the decoder. We have derived the entire achievable rate-distortion-equivocation region for this problem.
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| 31. |
- Gouverneur, Amaury, et al.
(författare)
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An Information-Theoretic Analysis of Bayesian Reinforcement Learning
- 2022
-
Ingår i: 2022 58Th Annual Allerton Conference On Communication, Control, And Computing (ALLERTON). - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Building on the framework introduced by Xu and Raginsky[1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learning problems. With this purpose, we define the minimum Bayesian regret (MBR) as the difference between the maximum expected cumulative reward obtainable either by learning from the collected data or by knowing the environment and its dynamics. We specialize this definition to reinforcement learning problems modeled as Markov decision processes (MDPs) whose kernel parameters are unknown to the agent and whose uncertainty is expressed by a prior distribution. One method for deriving upper bounds on the MBR is presented and specific bounds based on the relative entropy and the Wasserstein distance are given. We then focus on two particular cases of MDPs, the multi-armed bandit problem (MAB) and the online optimization with partial feedback problem. For the latter problem, we show that our bounds can recover from below the current information-theoretic bounds by Russo and Van Roy [2].
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| 32. |
- Gouverneur, Amaury, et al.
(författare)
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Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards
- 2023
-
Ingår i: 2023 IEEE International Symposium on Information Theory, ISIT 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1306-1311
-
Konferensbidrag (refereegranskat)abstract
- In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by [1] and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual information of the environment parameters and the history. Then, we introduce new bounds on the lifted information ratio that hold for sub-Gaussian rewards, thus generalizing the results from [1] which analysis requires binary rewards. Finally, we provide explicit regret bounds for the special cases of unstructured bounded contextual bandits, structured bounded contextual bandits with Laplace likelihood, structured Bernoulli bandits, and bounded linear contextual bandits.
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| 33. |
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| 34. |
- Le Treust, Mael, et al.
(författare)
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Continuous Random Variable Estimation is not Optimal for the Witsenhausen Counterexample
- 2021
-
Ingår i: 2021 IEEE International Symposium on Information Theory (ISIT). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1889-1894
-
Konferensbidrag (refereegranskat)abstract
- Optimal design of distributed decision policies can be a difficult task, illustrated by the famous Witsenhausen counterexample. In this paper we characterize the optimal control designs for the vector-valued setting assuming that it results in an interim state, i.e. the result of the first decision maker action, that can be described by a continuous random variable which has a probability density function. More specifically, we provide a genie-aided outer bound that relies on our previous results for empirical coordination problems. This solution turns out to be not optimal in general, since it consists of a time-sharing strategy between two linear schemes of specific power. It follows that the optimal decision strategy for the original scalar Witsenhausen problem must lead to an interim state that cannot be described by a continuous random variable which has a probability density function.
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| 35. |
- Le Treust, Mael, et al.
(författare)
-
Power-Estimation Trade-Off of Vector-Valued Witsenhausen Counterexample With Causal Decoder
- 2024
-
Ingår i: IEEE Transactions on Information Theory. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9448 .- 1557-9654. ; 70:3, s. 1588-1609
-
Tidskriftsartikel (refereegranskat)abstract
- The vector-valued extension of the famous Witsenhausen counterexample setup is studied where the encoder, i.e. the first decision maker, non-causally knows and encodes the i.i.d. state sequence and the decoder, i.e. the second decision maker, causally estimates the interim state. The coding scheme is transferred from the finite alphabet coordination problem, for which it is proved to be optimal. The extension to the Gaussian setup is based on a non-standard weak typicality approach and requires a careful average estimation error analysis since the interim state is estimated by the decoder. We provide a single-letter expression that characterizes the optimal trade-off between the Witsenhausen power cost and estimation cost. The two auxiliary random variables improve the communication with the decoder, while performing the dual role of the channel input, which also controls the state of the system. Interestingly, we show that a pair of discrete and continuous auxiliary random variables, outperforms both Witsenhausen two-point strategy and the best affine policies. The optimal choice of random variables remains unknown.
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| 36. |
- Pröbstel, Anne-Katrin, et al.
(författare)
-
Gut microbiota-specific IgA+ B cells traffic to the CNS in active multiple sclerosis
- 2020
-
Ingår i: Science immunology. - : American Association for the Advancement of Science. - 2470-9468. ; 5:53
-
Tidskriftsartikel (refereegranskat)abstract
- Changes in gut microbiota composition and a diverse role of B cells have recently been implicated in multiple sclerosis (MS), a central nervous system (CNS) autoimmune disease. Immunoglobulin A (IgA) is a key regulator at the mucosal interface. However, whether gut microbiota shape IgA responses and what role IgA+ cells have in neuroinflammation are unknown. Here, we identify IgA-bound taxa in MS and show that IgA-producing cells specific for MS-associated taxa traffic to the inflamed CNS, resulting in a strong, compartmentalized IgA enrichment in active MS and other neuroinflammatory diseases. Unlike previously characterized polyreactive anti-commensal IgA responses, CNS IgA cross-reacts with surface structures on specific bacterial strains but not with brain tissue. These findings establish gut microbiota-specific IgA+ cells as a systemic mediator in MS and suggest a critical role of mucosal B cells during active neuroinflammation with broad implications for IgA as an informative biomarker and IgA-producing cells as an immune subset to harness for therapeutic interventions.
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| 37. |
- Saeidian, Sara, et al.
(författare)
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Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage
- 2024
-
Ingår i: Privacy Technologies and Policy - 12th Annual Privacy Forum, APF 2024, Proceedings. - : Springer Nature. ; , s. 73-86
-
Konferensbidrag (refereegranskat)abstract
- Data-driven advancements significantly contribute to societal progress, yet they also pose substantial risks to privacy. In this landscape, differential privacy (DP) has become a cornerstone in privacy preservation efforts. However, the adequacy of DP in scenarios involving correlated datasets has sometimes been questioned and multiple studies have hinted at potential vulnerabilities. In this work, we delve into the nuances of applying DP to correlated datasets by leveraging the concept of pointwise maximal leakage (PML) for a quantitative assessment of information leakage. Our investigation reveals that DP’s guarantees can be arbitrarily weak for correlated databases when assessed through the lens of PML. More precisely, we prove the existence of a pure DP mechanism with PML levels arbitrarily close to that of a mechanism which releases individual entries from a database without any perturbation. By shedding light on the limitations of DP on correlated datasets, our work aims to foster a deeper understanding of subtle privacy risks and highlight the need for the development of more effective privacy-preserving mechanisms tailored to diverse scenarios.
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| 38. |
- Saeidian, Sara, et al.
(författare)
-
Optimal Maximal Leakage-Distortion Tradeoff
- 2021
-
Ingår i: 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Most methods for publishing data with privacy guarantees introduce randomness into datasets which reduces the utility of the published data. In this paper, we study the privacy-utility tradeoff by taking maximal leakage as the privacy measure and the expected Hamming distortion as the utility measure. We study three different but related problems. First, we assume that the data-generating distribution (i.e., the prior) is known, and we find the optimal privacy mechanism that achieves the smallest distortion subject to a constraint on maximal leakage. Then, we assume that the prior belongs to some set of distributions, and we formulate a min-max problem for finding the smallest distortion achievable for the worst-case prior in the set, subject to a maximal leakage constraint. Lastly, we define a partial order on privacy mechanisms based on the largest distortion they generate. Our results show that when the prior distribution is known, the optimal privacy mechanism fully discloses symbols with the largest prior probabilities, and suppresses symbols with the smallest prior probabilities. Furthermore, we show that sets of priors that contain more uniform distributions lead to larger distortion, while privacy mechanisms that distribute the privacy budget more uniformly over the symbols create smaller worst-case distortion. A full version of this paper is accessible at: https://arxiv.org/pdf/2105.01033.pdf
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| 39. |
- Saeidian, Sara, et al.
(författare)
-
Pointwise Maximal Leakage
- 2022
-
Ingår i: IEEE International Symposium on Information Theory - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 626-631
-
Konferensbidrag (refereegranskat)abstract
- Pointwise maximal leakage (PML) is a robust and operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X by disclosing a single outcome of a (randomized) function calculated on X. In this paper, we define a new privacy measure called event maximal leakage (EML), which generalizes PML by quantifying the amount of information leaking about X to arbitrary events. Then, we use our new privacy measure to define a new probabilistic privacy guarantee called (ϵ, δ)-EML. We study the data-processing and composition properties of (ϵ, δ)-EML and other privacy guarantees, where our goal is to understand whether or not they are closed under pre- and post-processing, and how they change as a result of adaptively composing privacy mechanisms.
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| 40. |
- Saeidian, Sara, 1994-
(författare)
-
Pointwise Maximal Leakage : Robust, Flexible and Explainable Privacy
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- For several decades now, safeguarding sensitive information from disclosure has been a key focus in computer science and information theory. Especially, in the past two decades, the subject of privacy has received significant attention due to the widespread collection and processing of data in various facets of society. A central question in this area is "What can be inferred about individuals from the data collected from them?"This doctoral thesis delves into a foundational and application-agnostic exploration of the theory of privacy. The overarching objective is to construct a comprehensive framework for evaluating and designing privacy-preserving data processing systems that adhere to three essential criteria:Explainability. The notion of information leakage (or privacy loss) employed in this framework should be operationally meaningful. That is, it should naturally emerge from the analysis of adversarial attack scenarios. Privacy guarantees within this framework should be comprehensible to stakeholders and the associated privacy parameters should be meaningful and interpretable. Robustness. The notion of information leakage employed should demonstrate resilience against a diverse array of potential adversaries, accommodating a broad range of attack scenarios while refraining from making restrictive assumptions about adversarial capabilities.Flexibility. The framework should offer value in a variety of contexts, catering to both highly privacy-sensitive applications and those with more relaxed privacy requirements. The notion of information leakage employed should also be applicable to various data types.The privacy notion proposed in this thesis that aligns with all the above criteria is called pointwise maximal leakage (PML). PML is a random variable that measures the amount of information leaking about a secret random variable X to a publicly available related random variable Y. We first develop PML for finite random variables by studying two seemingly different but mathematically equivalent adversarial setups: the randomized function model and the gain function model. We then extend the gain function model to random variables on arbitrary probability spaces to obtain a more general form of PML. Furthermore, we study the properties of PML in terms of pre and post-processing inequalities and composition, define various privacy guarantees, and compare PML with existing privacy notions from the literature including differential privacy and its local variant. PML, by definition, is an inferential privacy measure in the sense that it compares an adversary's posterior knowledge about X with her prior knowledge. However, a prevalent misconception in the area suggests that meaningful inferential privacy guarantees are unattainable, due to an over-interpretation of a result called the impossibility of absolute disclosure prevention. Through a pivotal shift in perspective, we characterize precisely the types of disclosures that can be prevented through privacy guarantees and those that remain inevitable. In this way, we argue in favor of inferential privacy measures. On the more application-oriented front, we examine a common machine learning framework for privacy-preserving learning called Private Aggregation of Teacher Ensembles (PATE) using an information-theoretic privacy measure. Specifically, we propose a conditional form of the notion of maximal leakage to quantify the amount of information leaking about individual data entries and prove that the leakage is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of the leakage implies that increased classification accuracy improves privacy. We also derive upper bounds on the information leakage when the injected noise has Laplace distribution.Finally, we design optimal privacy mechanisms that minimize Hamming distortion subject to maximal leakage constraints assuming that (i) the data-generating distribution (i.e., the prior) is known, or (ii) the prior belongs to a certain set of possible distributions. We prove that sets of priors that contain more "uniform" distributions generate larger distortion. We also prove that privacy mechanisms that distribute the privacy budget more uniformly over the outcomes create smaller worst-case distortion.
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| 41. |
- Saeidian, Sara, et al.
(författare)
-
Pointwise Maximal Leakage
- 2023
-
Ingår i: IEEE Transactions on Information Theory. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9448 .- 1557-9654. ; 69:12, s. 8054-8080
-
Tidskriftsartikel (refereegranskat)abstract
- We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-existing notion of maximal leakage, which quantifies the amount of information leaking about a secret X by disclosing a single outcome of a (randomized) function calculated on X. Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about X to adversaries seeking to guess arbitrary (possibly randomized) functions of X, or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre and post-processing, etc. Furthermore, we propose to view information leakage as a random variable which, in turn, allows us to regard privacy guarantees as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy, f-information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.
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| 42. |
- Saeidian, Sara, et al.
(författare)
-
Pointwise Maximal Leakage on General Alphabets
- 2023
-
Ingår i: 2023 IEEE International Symposium on Information Theory, ISIT 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 388-393
-
Konferensbidrag (refereegranskat)abstract
- Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X to a single outcome of a related random variable Y. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of X. Then, we consider adversaries who construct estimates of X that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.
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| 43. |
- Saeidian, Sara, et al.
(författare)
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Quantifying Membership Privacy via Information Leakage
- 2021
-
Ingår i: IEEE Transactions on Information Forensics and Security. - : Institute of Electrical and Electronics Engineers (IEEE). - 1556-6013 .- 1556-6021. ; 16, s. 3096-3108
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this paper, we propose an approach based on information leakage for guaranteeing membership privacy. Specifically, we propose to use a conditional form of the notion of maximal leakage to quantify the information leaking about individual data entries in a dataset, i.e., the entrywise information leakage. We apply our privacy analysis to the Private Aggregation of Teacher Ensembles (PATE) framework for privacy-preserving classification of sensitive data and prove that the entrywise information leakage of its aggregation mechanism is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of this leakage implies that increased consensus among teachers in labeling a query reduces its associated privacy cost. Finally, we derive upper bounds on the entrywise information leakage when the aggregation mechanism uses Laplace distributed noise.
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| 44. |
- Vu, Minh Thành, et al.
(författare)
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Hypothesis Testing and Identification Systems
- 2021
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Ingår i: IEEE Transactions on Information Theory. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9448 .- 1557-9654. ; 67:6, s. 3765-3780
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Tidskriftsartikel (refereegranskat)abstract
- We study hypothesis testing problems with fixed compression mappings and with user-dependent compression mappings to decide whether or not an observation sequence is related to one of the users in a database, which contains compressed versions of previously enrolled users' data. We first provide the optimal characterization of the exponent of the probability of the second type of error for the fixed compression mappings scenario when the number of users in the database grows exponentially. We then establish operational equivalence relations between the Wyner-Ahlswede-Korner network, the single-user hypothesis testing problem, the multi-user hypothesis testing problem with user-dependent compression mappings and the identification systems with user-dependent compression mappings. These equivalence relations imply the strong converse and exponentially strong converse for the multi-user hypothesis testing and the identification systems both with user-dependent compression mappings. Finally they also show how an identification scheme can be turned into a multi-user hypothesis testing scheme with an explicit transfer of rate and error probability conditions and vice versa.
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| 45. |
- Vu, Minh Thành, et al.
(författare)
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Uncertainty in Identification Systems
- 2021
-
Ingår i: IEEE Transactions on Information Theory. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9448 .- 1557-9654. ; 67:3, s. 1400-1414
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Tidskriftsartikel (refereegranskat)abstract
- High-dimensional identification systems consisting of two groups of users in the presence of statistical uncertainties are considered in this work. The task is to design enrollment mappings to compress users' information and an identification mapping that combines the stored information in the database and an observation to estimate the underlying user index. The compression-identification trade-off regions are established for the compound, extended compound, general and mixture settings. It is shown that several settings admit the same compression-identification trade-offs. We then study a connection between the Wyner-Ahlswede-Korner network and the identification setting. It indicates that a strong converse for the WAK network is equivalent to a strong converse for the identification setting. Finally, we present strong converse arguments for the discrete identification setting that are extensible to the Gaussian scenario.
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| 46. |
- You, Yang, et al.
(författare)
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Belief Function Fusion based Self-calibration for Non-dispersive Infrared Gas Sensor
- 2020
-
Ingår i: IEEE Sensors Conference. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-4
-
Konferensbidrag (refereegranskat)abstract
- Non-dispersive infrared gas sensing is one of the best gas measurement method for air quality monitoring. However, sensors drift over time due to sensor aging and environmental factors, which makes regular calibration necessary. In this paper, we first propose a general belief function fusion framework for NDIR gas sensor calibration, where we focus on getting a reasonable fused belief function of the true CO 2 level. To deal with belief functions highly conflict that may highly conflict with each other, we further propose a modified weighted average approach which utilizes the Wasserstein distance as a measure of the similarity between the belief functions. The numerical experiments show excellent initial results which confirms the belief function fusion framework for NDIR gas sensor is possible.
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| 47. |
- You, Yang, et al.
(författare)
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Energy Management Strategy for Smart Meter Privacy and Cost Saving
- 2021
-
Ingår i: IEEE Transactions on Information Forensics and Security. - : Institute of Electrical and Electronics Engineers (IEEE). - 1556-6013 .- 1556-6021. ; 16, s. 1522-1537
-
Tidskriftsartikel (refereegranskat)abstract
- We design optimal privacy-enhancing and cost-efficient energy management strategies for consumers that are equipped with a rechargeable energy storage. The Kullback-Leibler divergence rate is used as privacy measure and the expected cost-saving rate is used as utility measure. The corresponding energy management strategy is designed by optimizing a weighted sum of both privacy and cost measures over a finite time horizon, which is achieved by formulating our problem into a belief-state Markov decision process problem. A computationally efficient approximated Q-learning method is proposed as a generalization to high-dimensional problems over an infinite time horizon. At last, we explicitly characterize a stationary policy that achieves the steady belief state over an infinite time horizon, which greatly simplifies the design of the privacy-preserving energy management strategy. The performance of the practical design approaches are finally illustrated in numerical experiments.
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| 48. |
- You, Yang, et al.
(författare)
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Hidden Markov Model Based Data-driven Calibration of Nondispersive Infrared Gas Sensor
- 2020
-
Ingår i: Proceedings of EUSIPCO 2020. - : IEEE. ; , s. 1717-1721, s. 1717-1721
-
Konferensbidrag (refereegranskat)abstract
- Non-dispersive infrared gas sensing is one of the best gas measurement method for air quality monitoring. However, sensors drift over time due to sensor aging and environmental factors, which makes calibration necessary. In this paper, we propose a hidden Markov model approach for sensor self- calibration, which builds on the physical model of gas sensors based on the Beer-Lambert law. We focus on the statistical dependency between a calibration coefficient and the temperature change. Supervised and unsupervised learning algorithms to learn the stochastic parameters of the hidden Markov model are derived and numerically tested. The true calibration coefficient at each time instant is estimated using the Viterbi algorithm. The numerical experiments using CO2 sensor data show excellent initial results which confirms that data-driven calibration of non-dispersive infrared gas sensors is possible. Meanwhile, the challenge in the practical design is to find an appropriate quantization scheme to keep the computation burden reasonable while achieving good performance.
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| 49. |
- You, Yang
(författare)
-
Intelligent System Designs : Data-driven Sensor Calibration & Smart Meter Privacy
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Nowadays, the intelligent system has gained high popularity in successful implementation of real-time tasks due to its capability of providing efficient and powerful decision making in real applications. In this thesis, we aim for exploring and exploiting different concepts or methods to handle different tasks towards the intelligent system design. In particular, we focus on the following two problems: (i) Consumer-centric privacy-cost trade-off in smart metering system; (ii) Data-driven calibration for gas sensing system.For the first target problem, an optimal privacy-preserving and cost-efficient energy management strategy is designed for each smart grid consumer that is equipped with a rechargeable energy storage. The Kullback-Leibler divergence rate is used as privacy measure and the expected cost-saving rate is used as utility measure. The corresponding energy management strategy is designed by optimizing a weighted sum of both privacy and cost measures over a finite time horizon, which is achieved by formulating our problem into a partial observed Markov decision process problem. A computationally efficient approximated Q-learning method is proposed as a extension to high-dimensional problems over an infinite time horizon. Furthermore, the privacy-preserving and cost-efficient energy management strategy is designed for multiple smart grid consumers that are equipped with renewable energy sources. Different from the previous problem, the adversary is assumed to employ a factorial hidden Markov model based inference for load disaggregation, and the corresponding joint log-likelihood of the model is utilized as privacy measure. A dynamic pricing model is studied, where the price of unit amount of energy is determined by the consumers' aggregated power request, which suits a commodity-limited market. The consumers' energy management strategy is designed under a non-cooperative game framework by optimizing a weighted sum of both privacy measure and the user's energy cost savings. The consumers' non-cooperative game is shown to admit a unique pure strategy Nash equilibrium. As an extension, a computational-efficient distributed Nash equilibrium energy management strategy seeking method is proposed, which also avoids the privacy leakage due to the sharing of payoff functions between consumers.For the second target problem, several data-driven self-calibration algorithms are developed for low-cost non-dispersive infrared sensors. The measurement errors of the sensors are mainly caused by the remaining model errors and can be fully described by the drift of the calibration parameter. This leads to our first formulation of a statistical inference problem on the true calibration parameter under the HMM framework, which is a stochastic model that jointly builds on different quantities introduced by the physical model. To better track the time-varying drift process of the sensor, a time-adaptive expectation maximization learning framework is proposed to efficiently update the HMM parameters. For the joint calibration of the gas sensing system, sensors firsttransmit their belief functions of the true gas concentration levelto the cloud. Then the cloud fusion center computes a fusedbelief function according to certain rules. This belief functionis then used as reference for calibrating the sensors. To dealwith the case where belief functions highly conflict with eachother, a Wasserstein distance based weighted average belieffunction fusion approach is first proposed as networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep Q-network approach is applied as a computationally efficient method to solve the proposed Markovdecision process problem.The results in this thesis have shown that our proposed design frameworks can provide concise but precise mathematical models, proper problem formulations, and efficient solutions for the target design objectives of different intelligent systems.
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| 50. |
- You, Yang, et al.
(författare)
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Non-Cooperative Games for Privacy-Preserving and Cost-Efficient Smart Grid Energy Management
- 2023
-
Ingår i: IEEE Transactions on Information Forensics and Security. - : Institute of Electrical and Electronics Engineers (IEEE). - 1556-6013 .- 1556-6021. ; 18, s. 423-434
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we design privacy-preserving and cost-efficient energy management strategies for smart grid users that are equipped with renewable energy sources. The adversary is assumed to employ a factorial hidden Markov model based inference for load disaggregation, and the corresponding joint log-likelihood of the model is utilized as the privacy measure. The studied dynamic pricing model is applicable to a commodity-limited market, where the price of unit amount of energy is determined by the users' aggregated power request. The users' energy management strategies are designed under a non-cooperative game framework, where each user aims to optimize a weighted sum objective of both privacy measure and energy cost saving. The users' non-cooperative game is shown to admit a unique pure strategy Nash equilibrium. As an extension, a computational-efficient distributed Nash equilibrium energy management strategy seeking method is proposed, which also avoids the privacy leakage due to the sharing of payoff functions between users. The performance of practical designs of the energy management strategies in the equilibrium is finally illustrated by numerical experiments.
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