Authors: Anca Hangan, Dragos Lazea, Tudor Cioara

Published on: March 14, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.09322

Summary

• What is new: A novel privacy-preserving anomaly detection solution for homomorphically encrypted IoT data, utilizing a histogram-based technique adapted to the TFHE scheme.
• Why this is important: The increased vulnerability of IoT devices to malfunctions or cyberattacks and privacy concerns due to the necessity of decrypting data for anomaly detection.
• What the research proposes: A method that detects anomalies without decryption by adapting histogram-based anomaly detection for homomorphically encrypted data.
• Results: Effective anomaly detection comparable to non-encrypted data analysis, with resilience against IoT challenges and reasonable computational overhead.

Technical Details

Technological frameworks used: TFHE scheme

Models used: Histogram-based anomaly detection

Data used: Homomorphically encrypted IoT data

Potential Impact

Cybersecurity firms, IoT device manufacturers, cloud storage and computing services

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