Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets
This work addresses privacy concerns for organizations handling sensitive incremental data like network traffic, though it is incremental as it builds on existing cryptographic techniques.
The paper tackles the problem of performing outlier detection on sensitive incremental datasets without compromising privacy, by introducing a privacy-preserving protocol that decomposes the detection algorithm into phases with efficient cryptographic operations and sliding window updates, achieving moderate computation and communication costs.
Outlier detection is widely used in practice to track the anomaly on incremental datasets such as network traffic and system logs. However, these datasets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this paper, we present a privacy-preserving outlier detection protocol (PPOD) for incremental datasets. The protocol decomposes the outlier detection algorithm into several phases and recognises the necessary cryptographic operations in each phase. It realises several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted datasets. To support efficient updates, it integrates the sliding window model to periodically evict the expired data in order to maintain a constant update time. We build a prototype of PPOD and systematically evaluates the cryptographic modules and the overall protocols under various parameter settings. Our results show that PPOD can handle encrypted incremental datasets with a moderate computation and communication cost.