Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation

As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-pr...

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Bibliographic Details
Published inJournal of cybersecurity and privacy Vol. 3; no. 2; pp. 209 - 226
Main Authors Wang, Feng, Tang, Yongning, Fang, Hongbing
Format Journal Article
LanguageEnglish
Published Washington MDPI AG 01.06.2023
Subjects
Cameras
Data collection
data utility
Datasets
Internet of Things
IoT
Privacy
privacy leakage
Time series
traffic pattern
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Summary:As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations.
ISSN:2624-800X
2624-800X
DOI:10.3390/jcp3020012