PrivStream:数据流的差分私有事件检测

Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy Pub Date : 2019-03-13 DOI:10.1145/3292006.3302379

Maryam Fanaeepour, Ashwin Machanavajjhala

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引用次数: 2

摘要

实时系统中的事件监控和检测至关重要。在几乎实时地报告事件的同时保护用户数据将增加这一挑战的级别。在这项工作中，我们采用了强烈的差分隐私概念，以私有流计数进行事件检测，目的是最大限度地减少误报率和误报率作为我们的效用指标。

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PrivStream: Differentially Private Event Detection on Data Streams

Event monitoring and detection in real-time systems is crucial. Protecting users' data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private stream counting for event detection with the aim of minimizing false positive and false negative rates as our utility metrics.

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Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy

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