Enabling efficient privacy-assured outlier detection over encrypted incremental data sets

Shangqi Lai, Xingliang Yuan, Amin Sakzad, Mahsa Salehi, Joseph K. Liu, Dongxi Liu

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


Outlier detection is widely used in practice to track the anomaly on incremental data sets, such as network traffic and system logs. However, these data sets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this article, we present a privacy-preserving outlier detection (PPOD) protocol for incremental data sets. The protocol decomposes the outlier detection algorithm into several phases and recognizes the necessary cryptographic operations in each phase. It realizes 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 data sets. 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 data sets with a moderate computation and communication cost.

Original languageEnglish
Article number8882332
Pages (from-to)2651-2662
Number of pages12
JournalIEEE Internet of Things Journal
Issue number4
Publication statusPublished - Apr 2020


  • Outlier detection
  • secure computation

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