| 1. |
- Abril, Daniel, et al.
(författare)
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Spherical Microaggregation : Anonymizing Sparse Vector Spaces
- 2015
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Ingår i: Computers & security (Print). - : Elsevier. - 0167-4048 .- 1872-6208. ; 49, s. 28-44
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Tidskriftsartikel (refereegranskat)abstract
- Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets.
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| 2. |
- Abril, Daniel, et al.
(författare)
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Supervised Learning Using a Symmetric Bilinear Form for Record Linkage
- 2015
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 26, s. 144-153
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Tidskriftsartikel (refereegranskat)abstract
- Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk.In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions.
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| 3. |
- Adewole, Kayode S., et al.
(författare)
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DFTMicroagg: a dual-level anonymization algorithm for smart grid data
- 2022
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Ingår i: International Journal of Information Security. - : Springer. - 1615-5262 .- 1615-5270. ; 21, s. 1299-1321
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Tidskriftsartikel (refereegranskat)abstract
- The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.
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| 4. |
- Adewole, Kayode S., et al.
(författare)
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Energy disaggregation risk resilience through microaggregation and discrete Fourier transform
- 2024
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 662
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Tidskriftsartikel (refereegranskat)abstract
- Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.
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| 5. |
- Adewole, Kayode Sakariyah, et al.
(författare)
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Privacy issues in smart grid data : from energy disaggregation to disclosure risk
- 2022
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Ingår i: Database and expert systems applications. - Cham : Springer. - 9783031124228 - 9783031124235 ; , s. 71-84
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Konferensbidrag (refereegranskat)abstract
- The advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring (NILM). The development of robust AI and machine learning algorithms based on deep learning architecture has enabled accurate extraction of individual appliance load signature from aggregated energy data. However, the success rate of NILM algorithm in disaggregating individual appliance load signature in smart grid data violates the privacy of the individual household lifestyle. This paper investigates the performance of Sequence-to-Sequence (Seq2Seq) deep learning NILM algorithm in predicting the load signature of appliances. Furthermore, we define a new notion of disclosure risk to understand the risk associated with individual appliances in aggregated signals. Two publicly available energy disaggregation datasets have been considered. We simulate three inference attack scenarios to better ascertain the risk of publishing raw energy data. In addition, we investigate three activation extraction methods for appliance event detection. The results show that the disclosure risk associated with releasing smart grid data in their original form is on the high side. Therefore, future privacy protection mechanisms should devise efficient methods to reduce this risk.
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| 6. |
- Adewole, Kayode Sakariyah, et al.
(författare)
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Privacy protection of synthetic smart grid data simulated via generative adversarial networks
- 2023
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Ingår i: Proceedings of the 20th international conference on security and cryptography, SECRYPT 2023. - : SciTePress. - 9789897586668 ; , s. 279-286
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Konferensbidrag (refereegranskat)abstract
- The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.
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| 7. |
- Alcantud, Jose Carlos R., et al.
(författare)
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Decomposition theorems and extension principles for hesitant fuzzy sets
- 2018
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 41, s. 48-56
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Tidskriftsartikel (refereegranskat)abstract
- We prove a decomposition theorem for hesitant fuzzy sets, which states that every typical hesitant fuzzy set on a set can be represented by a well-structured family of fuzzy sets on that set. This decomposition is expressed by the novel concept of hesitant fuzzy set associated with a family of hesitant fuzzy sets, in terms of newly defined families of their cuts. Our result supposes the first representation theorem of hesitant fuzzy sets in the literature. Other related representation results are proven. We also define two novel extension principles that extend crisp functions to functions that map hesitant fuzzy sets into hesitant fuzzy sets.
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| 8. |
- Aliahmadipour, Laya, et al.
(författare)
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A definition for hesitant fuzzy partitions
- 2016
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Ingår i: International Journal of Computational Intelligence Systems. - : Taylor & Francis Group. - 1875-6891 .- 1875-6883. ; 9:3, s. 497-505
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.
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| 9. |
- Aliahmadipour, L., et al.
(författare)
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HFC : data clustering based on hesitant fuzzy decision making
- 2022
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Ingår i: Iranian Journal of Fuzzy Systems. - : University of Sistan and Baluchestan. - 1735-0654 .- 2676-4334. ; 19:5, s. 167-181
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Tidskriftsartikel (refereegranskat)abstract
- In a clustering task, choosing a proper clustering algorithm and obtaining qualified clusters are crucial issues. Sometimes, a clustering algorithm is chosen based on the data distribution, but data distributions are not known beforehand in real world problems. In this case, we hesitate which clustering algorithm to choose. In this paper, this hesitation is modeled by a hesitant fuzzy multi criteria decision making problem (HFMCDM) in which some clustering algorithms play the role of experts. Here, we consider fuzzy C-means (FCM) and agglomerative clustering algorithms as representative of two popular categories of clustering algorithms partitioning and hierarchical clustering methods, respectively.Then, we propose a new clustering procedure based on hesitant fuzzy decision making approaches (HFC) to decide which of the FCM family or hierarchical clustering algorithms is suitable for our data. This procedure ascertains a good clustering algorithm using neutrosophic FCM (NFCM) through a two phases process. The HFC procedure not only makes a true decision about applying partitioning clustering algorithms, but also improves the performance of FCM and evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) with construction hesitant fuzzy partition (HFP) conveniently. Experimental results show that the clustering procedure is applicable and practical. According to HFC procedure, it should be mentioned that it is possible to replace the other clustering algorithms that belong to any partitioning and hierarchical clustering methods. Also, we can consider other categories of clustering algorithms.
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| 10. |
- Aliahmadipour, Laya, et al.
(författare)
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On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
- 2017
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Ingår i: Fuzzy sets, rough sets, multisets and clustering. - Cham : Springer. - 9783319475561 - 9783319475578 ; , s. 157-168
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Bokkapitel (refereegranskat)abstract
- Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and initial parametrization of a clustering (classification) algorithm. Hesitant fuzzy set theory is a suitable tool to deal with this kind of problems. In this study, we introduce two different points of view to apply hesitant fuzzy sets in the data mining tasks, specially in the clustering algorithms.
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