FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling
FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling
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DOI：
10.1145/2463676.2465253
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Liyue Fan;Li Xiong;V. Sunderam
中科院分区：
文献类型：
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作者：
Liyue Fan;Li Xiong;V. Sunderam
当可以实时执行数据挖掘以了解流感爆发或交通拥堵等重要现象时，共享私有数据的汇总统计数据可能具有很大的价值。然而，到目前为止，还没有发布具有差异隐私的实时聚合数据的工具，这是一种强大且可证明的隐私保证。我们提出FAST，一个实时系统，允许差分私人聚合共享和时间序列分析。FAST采用一组新颖的自适应策略来提高共享/发布数据的效用，同时保证用户指定的差异隐私级别。我们将在准备好的数据集以及在SIGMOD'13与会者中动态收集的现场参与数据的背景下展示挑战和我们的解决方案。
Sharing aggregate statistics of private data can be of great value when data mining can be performed in real-time to understand important phenomena such as influenza outbreaks or traffic congestion. However, to this date there have been no tools for releasing real-time aggregated data with differential privacy, a strong and provable privacy guarantee. We propose FAST, a real-time system that allows differentially private aggregate sharing and time-series analytics. FAST employs a set of novel, adaptive strategies to improve the utility of shared/released data while guaranteeing the user-specified level of differential privacy. We will demonstrate the challenges and our solutions in the context of prepared data sets as well as live participation data dynamically collected among the SIGMOD'13 attendees.