Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms
Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/5/1260 |
| _version_ | 1797614837815050240 |
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| author | Adrian-Silviu Roman |
| author_facet | Adrian-Silviu Roman |
| author_sort | Adrian-Silviu Roman |
| collection | DOAJ |
| description | Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a new procedure for evaluating the utility and privacy of perturbation techniques and an algorithm for comparing perturbation methods. The contribution is significant for those involved in protecting time-series data collected from various sensors as the approach is sensor-type-independent, algorithm-independent, and data-independent. The analysis of the impact of data integrity attacks on the perturbed data follows the methodology. Experimental results obtained using actual data collected from a VW Passat vehicle via the OBD-II port demonstrate the applicability of the approach to measuring the utility and privacy of perturbation algorithms. Moreover, important benefits have been identified: the proposed approach measures both privacy and utility, various distortion and perturbation methods can be compared (no matter how different), and an evaluation of the impact of data integrity attacks on perturbed data is possible. |
| first_indexed | 2024-03-11T07:17:55Z |
| format | Article |
| id | doaj.art-bec58b638b6943bbac3c418b3561083a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T07:17:55Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-bec58b638b6943bbac3c418b3561083a2023-11-17T08:10:25ZengMDPI AGMathematics2227-73902023-03-01115126010.3390/math11051260Evaluating the Privacy and Utility of Time-Series Data Perturbation AlgorithmsAdrian-Silviu Roman0Department of Electrical Engineering and Information Technology, Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, RomaniaData collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a new procedure for evaluating the utility and privacy of perturbation techniques and an algorithm for comparing perturbation methods. The contribution is significant for those involved in protecting time-series data collected from various sensors as the approach is sensor-type-independent, algorithm-independent, and data-independent. The analysis of the impact of data integrity attacks on the perturbed data follows the methodology. Experimental results obtained using actual data collected from a VW Passat vehicle via the OBD-II port demonstrate the applicability of the approach to measuring the utility and privacy of perturbation algorithms. Moreover, important benefits have been identified: the proposed approach measures both privacy and utility, various distortion and perturbation methods can be compared (no matter how different), and an evaluation of the impact of data integrity attacks on perturbed data is possible.https://www.mdpi.com/2227-7390/11/5/1260data privacydata perturbationtime-series perturbationdata miningautomotive systems |
| spellingShingle | Adrian-Silviu Roman Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms Mathematics data privacy data perturbation time-series perturbation data mining automotive systems |
| title | Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms |
| title_full | Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms |
| title_fullStr | Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms |
| title_full_unstemmed | Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms |
| title_short | Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms |
| title_sort | evaluating the privacy and utility of time series data perturbation algorithms |
| topic | data privacy data perturbation time-series perturbation data mining automotive systems |
| url | https://www.mdpi.com/2227-7390/11/5/1260 |
| work_keys_str_mv | AT adriansilviuroman evaluatingtheprivacyandutilityoftimeseriesdataperturbationalgorithms |