Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Data Sets

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.

AI for Health-Related Data Modeling

“…In (Wahid & Annavarapu, 2021), an unsupervised density-based outlier detection algorithm was presented to deal with parameter selection problem. In (Lai et al, 2020), a privacy-preserving outlier detection protocol for incremental data sets was presented. The protocol decomposed the outlier detection algorithm into several phases and recognized the necessary cryptographic operations in each phase.…”

Research on executing equipment control algorithm based on multi-sensor pipe gallery

“…From the existing literature achievements, a lot of research work has been carried out on data anomalies in the field of big data analysis, and certain data anomaly detection methods and algorithms have been obtained, which provides a useful reference for further research. Among them are the research on the data quality of the Internet of Things [4] , Research on the detection in encrypted data sets [5] , Research on the abnormal detection of geological data [6] , Research on the detection of abnormal data in medical treatment, environment and smart grid [7] , Research on the detection of abnormal data in smart cities [8] class. However, in these studies, there are very few studies on abnormal data detection and environmental correction in the corridor when abnormal data may occur.…”