Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors

[2403.09322] Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2403.09322 (cs) [Submitted on 14 Mar 2024] Title: Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors Authors: Anca Hangan , Dragos Lazea , Tudor Cioara View a PDF of the paper titled Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors, by Anca Hangan and 2 other authors View PDF Abstract: IoT devices have become indispensable components of our lives, and the advancement of AI technologies will make them even more pervasive, increasing the vulnerability to malfunctions or cyberattacks and raising privacy concerns. Encryption can mitigate these challenges; however, most existing anomaly detection techniques decrypt the data to perform the analysis, potentially undermining the encryption protection provided during transit or storage. Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted, however, these schemes offer only limited operations, which poses challenges to their practical usage. In this paper, we propose a novel privacy-preserving anomaly detection solution designed for homomorphically encrypted data generated by IoT devices that efficiently detects abnormal values without performing decryption. We have adapted the Histogram-based anomaly detection technique for TFHE scheme to address limitations related to the input size and the depth of computation by implementing vectorized support operations. These operations include addition, value placement in buckets, labeling abnormal buckets based on a threshold frequency, labeling abnormal values based on their range, and bucket labels. Evaluation results show that the solution effectively detects anomalies without requiring data decryption and achieves consistent results comparable to the mechanism operating on plain data. Also, it shows robustness and resilience against various challenges commonly encountered in IoT environments, such as noisy sensor data, adversarial attacks, communication failures, and device malfunctions. Moreover, the time and computational overheads determined for several solution configurations, despite being large, are reasonable compared to those reported in existing literature. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2403.09322 [cs.CR] (or arXiv:2403.09322v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2403.09322 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.3390/fi17060256 Focus to learn more DOI(s) linking to related resources Submission history From: Anca Hangan [ view email ] [v1] Thu, 14 Mar 2024 12:11:25 UTC (1,621 KB) Full-text links: Access Paper: View a PDF of the paper titled Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors, by Anca Hangan and 2 other authors View PDF view license Current browse context: cs.CR < prev | next > new | recent | 2024-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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