| 1. |
- Arsalan, Muhammad, et al.
(författare)
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Energy-Efficient Privacy-Preserving Time-Series Forecasting on User Health Data Streams
- 2022
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Ingår i: Proceedings - 2022 IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 541-546
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Konferensbidrag (refereegranskat)abstract
- Health monitoring devices are gaining popularity both as wellness tools and as a source of information for healthcare decisions. In this work, we use Spiking Neural Networks (SNNs) for time-series forecasting due to their proven energy-saving capabilities. Thanks to their design that closely mimics the natural nervous system, SNNs are energy-efficient in contrast to classic Artificial Neural Networks (ANNs). We design and implement an energy-efficient privacy-preserving forecasting system on real-world health data streams using SNNs and compare it to a state-of-the-art system with Long short-term memory (LSTM) based prediction model. Our evaluation shows that SNNs tradeoff accuracy (2.2x greater error), to grant a smaller model (19% fewer parameters and 77% less memory consumption) and a 43% less training time. Our model is estimated to consume 3.36 mu J energy, which is significantly less than the traditional ANNs. Finally, we apply epsilon-differential privacy for enhanced privacy guarantees on our federated learning-based models. With differential privacy of epsilon = 0.1, our experiments report an increase in the measured average error (RMSE) of only 25%.
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| 2. |
- Fedeli, Stefano, et al.
(författare)
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Privacy Preserving Survival Prediction
- 2021
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Ingår i: 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4600-4608
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Konferensbidrag (refereegranskat)abstract
- Predictive modeling has the potential to improve risk stratification of cancer patients and thereby contribute to optimized treatment strategies and better outcomes for patients in clinical practice. To develop robust predictive models for decision-making in healthcare, sensitive patient-level data is often required when developing the training models. Consequently, data privacy is an important aspect to consider when building these predictive models and in subsequent communication of the results. In this study we have used Graph Neural Networks for survival prediction, and compared the accuracy to state-of-the-art prediction models after applying Differential Privacy and k-Anonymity, i.e. two privacy-preservation solutions. By using two different data sources we demonstrated that Graph Neural Networks and Survival Forests are the two most well-performing survival prediction methods when used in combination with privacy preservation solutions. Furthermore, when the predictive model was built using clinical expertise in the specific area of interest, the prediction accuracy of the proposed knowledge based graph model drops by at most 10% when used with privacy preservation solutions. Our proposed knowledge based graph is therefore more suitable to be used in combination with privacy preservation solutions as compared to other graph models.
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| 3. |
- Imtiaz, Sana, et al.
(författare)
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Machine Learning with Reconfigurable Privacy on Resource-Limited Computing Devices
- 2021
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Ingår i: 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1592-1602
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Konferensbidrag (refereegranskat)abstract
- Ensuring user privacy while learning from the acquired Internet of Things sensor data, using limited available compute resources on edge devices, is a challenging task. Ideally, it is desirable to make all the features of the collected data private but due to resource limitations, it is not always possible as it may cause overutilization of resources, which in turn affects the performance of the whole system. In this work, we use the generalization techniques for data anonymization and provide customized injective privacy encoder functions to make data features private. Regardless of the resource availability, some data features must be essentially private. All other data features that may pose low privacy threat are termed as nonessential features. We propose Dynamic Iterative Greedy Search (DIGS), a novel approach with corresponding algorithms to select the set of optimal data features to be private for machine learning applications provided device resource constraints. DIGS selects the necessary and the most private version of data for the application, where all essential and a subset of nonessential features are made private on the edge device without resource overutilization. We have implemented DIGS in Python and evaluated it on Raspberry Pi model A (an edge device with limited resources) for an SVM-based classification on real-life health care data. Our evaluation results show that, while providing the required level of privacy, DIGS allows to achieve up to 26.21% memory, 16.67% CPU instructions, and 30.5% of network bandwidth savings as compared to making all the data private. Moreover, our chosen privacy encoding method has a positive impact on the accuracy of the classification model for our chosen application.
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| 4. |
- Imtiaz, Sana, et al.
(författare)
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On the case of privacy in the iot ecosystem : a survey
- 2019
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Ingår i: Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1015-1024
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Konferensbidrag (refereegranskat)abstract
- IoT has enabled the creation of a multitude of personal applications and services for a better understanding of urban environments and our personal lives. These services are driven by the continuous collection and analysis of user data in order to provide personalized experiences. However, there is a strong need to address user privacy concerns as most of the collected data is of sensitive nature. This paper provides an overview of privacy preservation techniques and solutions proposed so far in literature along with the IoT levels at which privacy is addressed by each solution as well as their robustness to privacy breaching attacks. An analysis of functional and non-functional limitations of each solution is done, followed by a short survey of machine learning applications designed with these solutions. We identify open issues in the privacy preserving solutions when used in IoT environments. Moreover, we note that most of the privacy preservation solutions need to be adapted in the light of GDPR to accommodate the right to privacy of the users.
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| 5. |
- Imtiaz, Sana
(författare)
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Privacy preserving behaviour learning for the IoT ecosystem
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- IoT has enabled the creation of a multitude of personal applications and services for a better understanding and improvement of urban environments and our personal lives. These services are driven by the continuous collection and analysis of sensitive and private user data to provide personalised experiences. Among the different application areas of IoT, smart health care, in particular, necessitates the usage of privacy preservation techniques in order to guarantee protection from user privacy-breaching threats such as identification, profiling, localization and tracking, and information linkage. Traditional privacy preservation techniques such as pseudonymization are no longer sufficient to cater to the requirements of privacy preservation in the fast-growing smart health care domain due to the challenges offered by big data volume, velocity, and variety. On the other hand, there is a number of modern privacy preservation techniques with respective overheads that may have a negative impact on application performance such as reduced accuracy, reduced data utility, and increased device resource usage. There is a need to select appropriate privacy preservation techniques (and solutions) according to the nature of data, system performance requirements, and resource constraints, in order to find proper trade-offs between providing privacy preservation, data utility, and acceptable system performance in terms of accuracy, runtime, and resource consumption.In this work, we investigate different privacy preservation solutions and measure the impact of introducing our selected privacy preservation solutions on the performance of different components of the IoT ecosystem in terms of data utility and system performance. We implement, illustrate, and evaluate the results of our proposed approaches using real-world and synthetic privacy-preserving smart health care datasets. First, we provide a detailed taxonomy and analysis of the privacy preservation techniques and solutions which may serve as a guideline for selecting appropriate techniques according to the nature of data and system requirements. Next, in order to facilitate privacy preserving data sharing, we present and implement a method for creating realistic synthetic and privacy-preserving smart health care datasets using Generative Adversarial Networks and Differential Privacy. Later, we also present and develop a solution for privacy preserving data analytics, a differential privacy library PyDPLib, with health care data as a use case.In order to find proper trade-offs between providing necessary privacy preservation, device resource consumption, and application accuracy, we present and implement a novel approach with corresponding algorithms and an end-to-end system pipeline for reconfigurable data privacy in machine learning on resource-limited computing devices. Our evaluation results show that, while providing the required level of privacy, our proposed approach allows us to achieve up to 26.21% memory, 16.67% CPU instructions, and 30.5% of network bandwidth savings as compared to making all the data private. Moreover, we also present and implement an end-to-end solution for privacy-preserving time-series forecasting of user health data streams using Federated Learning and Differential Privacy. Our proposed solution finds a proper trade-off between providing necessary privacy preservation, application accuracy, and runtime, and at best introduces a decrease of ~2% in the prediction accuracy of the trained models.
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| 6. |
- Imtiaz, Sana, et al.
(författare)
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Privacy Preserving Time-Series Forecasting of User Health Data Streams
- 2020
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Ingår i: <em>2020 IEEE International Conference on Big Data (Big Data)</em>. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3428-3437
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Konferensbidrag (refereegranskat)abstract
- Privacy preservation plays a vital role in health care applications as the requirements for privacy preservation are very strict in this domain. With the rapid increase in the amount, quality and detail of health data being gathered with smart devices, new mechanisms are required that can cope with the challenges of large scale and real-time processing requirements. Federated learning (FL) is one of the conventional approaches that facilitate the training of AI models without access to the raw data. However, recent studies have shown that FL alone does not guarantee sufficient privacy. Differential privacy (DP) is a well-known approach for privacy guarantees, however, because of the noise addition, DP needs to make a trade-off between privacy and accuracy. In this work, we design and implement an end-to-end pipeline using DP and FL for the first time in the context of health data streams. We propose a clustering mechanism to leverage the similarities between users to improve the prediction accuracy as well as significantly reduce the model training time. Depending on the dataset and features, our predictions are no more than 0.025% far off the ground-truth value with respect to the range of value. Moreover, our clustering mechanism brings a significant reduction in the training time, with up to 49% reduction in prediction accuracy error in the best case, as compared to training a single model on the entire dataset. Our proposed privacy preserving mechanism at best introduces a decrease of ≈ 2% in the prediction accuracy of the trained models. Furthermore, our proposed clustering mechanism reduces the prediction error even in highly noisy settings by as much as 38% as compared to using a single federated private model.
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| 7. |
- Imtiaz, Sana, et al.
(författare)
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PyDPLib : Python Differential Privacy Library for Private Medical Data Analytics
- 2021
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Ingår i: Proceedings - 2021 IEEE International Conference on Digital Health, ICDH 2021. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 191-196
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Konferensbidrag (refereegranskat)abstract
- Pharmaceutical and medical technology companies accessing real-world medical data are not interested in personally identifiable data but rather in cohort data such as statistical aggregates, patterns, and trends. These companies cooperate with medical institutions that collect medical data and want to share it but they need to protect the privacy of individuals on the shared data. We present PyDPLib, a Python Differential Privacy library for private medical data analytics. We illustrate an application of differential privacy using PyDPLib in our platform for visualizing private statistics on a database of prostate cancer patients. Our experimental results show that PyDPLib allows creating statistical data plots without compromising patients' privacy while preserving underlying data distributions. Even though PyDPLib has been developed to be used in our platform for reporting the radiological examinations and procedures, it is general enough to be used to provide differential privacy on data in any data analytics and visualization platform, service or application.
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| 8. |
- Imtiaz, Sana, et al.
(författare)
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Synthetic and Private Smart Health Care Data Generation using GANs
- 2021
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Ingår i: 30th International Conference on Computer Communications and Networks (ICCCN 2021). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- With the rapid advancements in machine learning, the health care paradigm is shifting from treatment towards prevention. The smart health care industry relies on the availability of large-scale health datasets in order to benefit from machine learning-based services. As a consequence, preserving the individuals' privacy becomes vital for sharing sensitive personal information. Synthetic datasets with generative models are considered to be one of the most promising solutions for privacy-preserving data sharing. Among the generative models, generative adversarial networks (GANs) have emerged as the most impressive models for synthetic data generation in recent times. However, smart health care data is attributed with unique challenges such as volume, velocity, and various data types and distributions. We propose a GAN coupled with differential privacy mechanisms for generating a realistic and private smart health care dataset. The proposed approach is not only able to generate realistic synthetic data samples but also the differentially private data samples under different settings: learning from a noisy distribution or noising the learned distribution. We tested and evaluated our proposed approach using a real-world Fitbit dataset. Our results indicate that our proposed approach is able to generate quality synthetic and differentially private dataset that preserves the statistical properties of the original dataset.
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| 9. |
- Khan, Arslan, et al.
(författare)
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Comprehensive investigation of almond shells pyrolysis using advance predictive models
- 2024
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Ingår i: Renewable energy. - : Elsevier. - 0960-1481 .- 1879-0682. ; 227
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Tidskriftsartikel (refereegranskat)abstract
- This research focused on comprehensive characterization and assessment of almond shells pyrolysis for bioenergy potential through thermogravimetric analysis from ambient temperature to 900 °C at different heating rates of 10, 15, and 20 °C/min in inert environment. Iso-conversional model-free methods like Friedman, Ozawa-Flynn-Wall (OFW), and Kissinger-Akahira-Sunose (KAS) were used for kinetic analysis. Average activation energies (Ea) evaluated using Friedman, OFW, and KAS methods were 198.45 kJ mol−1, 204.43 kJ mol−1, and 204.97 kJ mol−1, respectively. The evaluation of thermodynamic parameters, including ΔH‡, ΔG‡, and ΔS‡, was also assessed. The average values of ΔH‡, ΔG‡, and ΔS‡, were found to be 199.4 kJ mol−1, 172.17 kJ mol−1 and 42.60 kJ mol−1 respectively. The reaction mechanism was obtained from combined kinetics. A high R2 value of 0.9933 demonstrates strong agreement between the combined kinetic analysis results and the experimental data. The distribution activation energy model was assessed employing four pseudo elements identified as PC1, PC2, PC3, and PC4. Artificial Neural Network (ANN) and Boosting regression trees (BRT) were used for the prediction of Ea of almond shells pyrolysis. The detailed understanding of thermokinetics and creating customized predictive and innovative modelling techniques like ANN and BRT sets a new benchmark for developing customized models for thermochemical conversion of varieties of almond shells.
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