Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets

TL;DR

This paper introduces PPOD, a privacy-preserving protocol for outlier detection on encrypted incremental datasets, combining cryptographic modules and a sliding window model for efficient updates.

Contribution

It presents a novel cryptographic protocol that enables outlier detection over encrypted incremental data with efficient update capabilities.

Findings

PPOD handles encrypted datasets with moderate computational and communication costs.

The protocol effectively supports incremental updates using a sliding window model.

Prototype evaluations demonstrate practical efficiency of the proposed method.

Abstract

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.