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.

中文翻译：

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在加密的增量数据集上实现高效的、保证隐私的异常值检测


异常值检测在实践中广泛用于跟踪增量数据集的异常，例如网络流量和系统日志。然而，这些数据集通常涉及敏感信息，将数据共享给第三方进行异常检测会引发隐私问题。在本文中，我们提出了一种用于增量数据集的隐私保护异常值检测（PPOD）协议。该协议将异常值检测算法分解为几个阶段，并识别每个阶段中必要的加密操作。它通过高效且可互换的协议实现了多个密码模块来支持上述密码操作，并将它们组合在整体协议中以实现对加密数据集的异常检测。为了支持高效更新，它集成了滑动窗口模型，定期驱逐过期数据，以保持恒定的更新时间。我们构建了 PPOD 原型，并系统地评估了各种参数设置下的密码模块和整体协议。我们的结果表明，PPOD 可以以适度的计算和通信成本处理加密的增量数据集。