Privacy-preserving real-time anomaly detection using edge computing

Anomaly detection on data collected by devices, such as sensors and IoT objects, is inevitable for many critical systems, e.g., an anomaly in the data of a patient's health monitoring device may indicate a medical emergency situation. Because of the resource-constrained nature of these devices, data collected by such devices are usually off-loaded to the cloud/edge for storage and/or further analysis. However, to ensure data privacy it is critical that the data be transferred to and managed by the cloud/edge in an encrypted form which necessitates efficient processing of such encrypted data for real-time anomaly detection. Motivated by the simultaneous demands for data privacy and real-time data processing, in this paper, we investigate the problem of a privacy-preserving real-time anomaly detection service on sensitive, time series, streaming data. We propose a privacy-preserving framework that enables efficient anomaly detection on encrypted data by leveraging a lightweight and aggregation optimized encryption scheme to encrypt the data before off-loading the data to the edge. We demonstrate our solution for a widely used anomaly detection algorithm, windowed Gaussian anomaly detector and evaluate the performance of the solution in terms of the obtained model privacy, accuracy, latency, and communication cost.