无限域上演化数据集的时间折扣差分隐私
Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon
摘要：
我们定义了折扣差分隐私，作为（传统的）差分隐私的替代方案，调查隐私不断变化的数据集，包含时间序列在一个无限的地平线。我们使用隐私损失来衡量报告在某个固定时间泄露的信息量。我们观察到，在差分隐私的定义中，隐私损失在时间上是相等加权的，因此，隐私保护的加性噪声的大小必须无限制地增长，以确保在无限范围内的差分隐私。受经济学文献中的贴现效用理论的启发，我们使用指数和双曲线贴现的隐私损失随着时间的推移，放宽连续观察下的差分隐私的定义。这意味着，对个人来说，遥远过去的隐私损失不如现在的重要。我们使用折扣差分隐私调查隐私的不断变化的数据集使用加性拉普拉斯噪声，并表明加性噪声的幅度可以保持有界折扣差分隐私。我们说明了隐私保护机制的质量，满足折扣差分隐私的智能电表测量时间序列的真实家庭，公开提供的Ausgrid（澳大利亚配电公司）。
英文摘要：
We define discounted differential privacy, as an alternative to (conventional) differential privacy, to investigate privacy of evolving datasets, containing time series over an unbounded horizon. We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time. We observe that privacy losses are weighted equally across time in the definition of differential privacy, and therefore the magnitude of privacy-preserving additive noise must grow without bound to ensure differential privacy over an infinite horizon. Motivated by the discounted utility theory within the economics literature, we use exponential and hyperbolic discounting of privacy losses across time to relax the definition of differential privacy under continual observations. This implies that privacy losses in distant past are less important than the current ones to an individual. We use discounted differential privacy to investigate privacy of evolving datasets using additive Laplace noise and show that the magnitude of the additive noise can remain bounded under discounted differential privacy. We illustrate the quality of privacy-preserving mechanisms satisfying discounted differential privacy on smart-meter measurement time-series of real households, made publicly available by Ausgrid (an Australian electricity distribution company).
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