IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1260-d1088494.html
   My bibliography  Save this article

Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms

Author

Listed:
  • Adrian-Silviu Roman

    (Department 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, Romania)

Abstract

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.

Suggested Citation

  • Adrian-Silviu Roman, 2023. "Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms," Mathematics, MDPI, vol. 11(5), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1260-:d:1088494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1260/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/5/1260/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Genge, Béla & Kiss, István & Haller, Piroska, 2015. "A system dynamics approach for assessing the impact of cyber attacks on critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 3-17.
    2. Eunmok Yang & Velmurugan Subbiah Parvathy & P. Pandi Selvi & K. Shankar & Changho Seo & Gyanendra Prasad Joshi & Okyeon Yi, 2020. "Privacy Preservation in Edge Consumer Electronics by Combining Anomaly Detection with Dynamic Attribute-Based Re-Encryption," Mathematics, MDPI, vol. 8(11), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hayes, Darren R. & Cappa, Francesco, 2018. "Open-source intelligence for risk assessment," Business Horizons, Elsevier, vol. 61(5), pages 689-697.
    2. Siwar Kriaa & Marc Bouissou & Youssef Laarouchi, 2019. "A new safety and security risk analysis framework for industrial control systems," Journal of Risk and Reliability, , vol. 233(2), pages 151-174, April.
    3. González, Santiago G. & Dormido Canto, S. & Sánchez Moreno, José, 2020. "Obtaining high preventive and resilience capacities in critical infrastructure by industrial automation cells," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    4. Chatzis, Petros & Stavrou, Eliana, 2022. "Cyber-threat landscape of border control infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 36(C).
    5. Qianxiang Zhu & Yuanqing Qin & Chunjie Zhou & Weiwei Gao, 2018. "Extended multilevel flow model-based dynamic risk assessment for cybersecurity protection in industrial production systems," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    6. Galbraith, John W. & Iuliani, Luca, 2019. "Measures of robustness for networked critical infrastructure: An empirical comparison on four electrical grids," International Journal of Critical Infrastructure Protection, Elsevier, vol. 27(C).
    7. Ahmed Shokry & Piero Baraldi & Andrea Castellano & Luigi Serio & Enrico Zio, 2021. "Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines," Energies, MDPI, vol. 14(18), pages 1-19, September.
    8. Jialu Hao & Wei Wu & Shuo Wang & Xiaoge Zhong & Guang Chu & Feng Shao, 2022. "A Delegation Attack Method on Attribute-Based Signatures and Probable Solutions," Mathematics, MDPI, vol. 11(1), pages 1-14, December.
    9. Leszczyna, Rafał, 2018. "Standards on cyber security assessment of smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 70-89.
    10. Palleti, Venkata Reddy & Joseph, Jude Victor & Silva, Arlindo, 2018. "A contribution of axiomatic design principles to the analysis and impact of attacks on critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 23(C), pages 21-32.
    11. Alberto Sardi & Enrico Sorano, 2019. "Dynamic Performance Management: An Approach for Managing the Common Goods," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    12. Bhandari, Pratik & Creighton, Douglas & Gong, Jinzhe & Boyle, Carol & Law, Kris M.Y., 2023. "Evolution of cyber-physical-human water systems: Challenges and gaps," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    13. Genge, Béla & Haller, Piroska & Kiss, István, 2016. "A framework for designing resilient distributed intrusion detection systems for critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 15(C), pages 3-11.
    14. Baraldi, Piero & Castellano, Andrea & Shokry, Ahmed & Gentile, Ugo & Serio, Luigi & Zio, Enrico, 2020. "A Feature Selection-based Approach for the Identification of Critical Components in Complex Technical Infrastructures: Application to the CERN Large Hadron Collider," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    15. Plotnek, Jordan J. & Slay, Jill, 2021. "Power systems resilience: Definition and taxonomy with a view towards metrics," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).
    16. Michał Syfert & Andrzej Ordys & Jan Maciej Kościelny & Paweł Wnuk & Jakub Możaryn & Krzysztof Kukiełka, 2022. "Integrated Approach to Diagnostics of Failures and Cyber-Attacks in Industrial Control Systems," Energies, MDPI, vol. 15(17), pages 1-24, August.
    17. A. Sardi & E. Sorano, 2021. "Dynamic Performance Management: An Approach for Managing the Common Goods," Papers 2102.04090, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1260-:d:1088494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.