  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.
