Identifying Anomalies While Preserving Privacy
Identifying Anomalies While Preserving Privacy
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DOI：
10.1109/tkde.2021.3129633
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发表时间：
2023-12
影响因子：
8.9
通讯作者：
H. Asif;Jaideep Vaidya;Periklis A. Papakonstantinou
中科院分区：
文献类型：
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作者：
H. Asif;Jaideep Vaidya;Periklis A. Papakonstantinou
识别数据中的异常在许多领域至关重要，包括医学、金融和国家安全。然而，隐私问题对进行此类分析构成了重大障碍。由于现有的隐私定义在进行离群值分析时不允许有很好的准确性，最近提出了敏感隐私的概念来处理这个问题。敏感隐私使得以实际有意义的准确性分析数据异常成为可能，同时提供类似于差分隐私的有力保证，这是当今流行的隐私标准。在这项工作中，我们将敏感隐私与其他重要的数据隐私概念联系起来，以便人们可以将这些相关概念的技术发展和隐私机制构建移植到敏感隐私中。敏感隐私严重依赖于底层异常模型。我们开发了一种新颖的n步前瞻机制来有效地回答任意异常点查询，当我们将注意力限制在常见的一类异常模型上时，可以证明它保证了敏感的隐私。我们还提供一般结构，以提供敏感的私有机制来识别异常，并显示结构最优的条件。
Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy concerns pose a significant roadblock to carrying out such an analysis. Since existing privacy definitions do not allow good accuracy when doing outlier analysis, the notion of sensitive privacy has been recently proposed to deal with this problem. Sensitive privacy makes it possible to analyze data for anomalies with practically meaningful accuracy while providing a strong guarantee similar to differential privacy, which is the prevalent privacy standard today. In this work, we relate sensitive privacy to other important notions of data privacy so that one can port the technical developments and private mechanism constructions from these related concepts to sensitive privacy. Sensitive privacy critically depends on the underlying anomaly model. We develop a novel n-step lookahead mechanism to efficiently answer arbitrary outlier queries, which provably guarantees sensitive privacy if we restrict our attention to common a class of anomaly models. We also provide general constructions to give sensitively private mechanisms for identifying anomalies and show the conditions under which the constructions would be optimal.