上下文感知本地信息隐私的拉普拉斯机制

Mohamed Seif, R. Tandon, Ming Li
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引用次数: 4

摘要

在本文中,我们考虑了在局部信息隐私约束下设计数据发布的加性噪声机制的问题。虽然在为差分隐私设计加性噪声机制(如拉普拉斯和高斯机制)方面已经有了大量的工作,但对于信息隐私的概念,它解释了关于数据的先验知识,没有这种通用的加性噪声机制。为此,我们设计了一种先验感知的拉普拉斯噪声机制,满足了局部信息的隐私性。我们表明,与上下文不知情机制相比,添加上下文感知(即通过对数据的先验知识)可以改善效用和隐私之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context Aware Laplacian Mechanism for Local Information Privacy
In this paper, we consider the problem of designing additive noise mechanisms for data release subject to a local information privacy constraint. While there has been significant prior work on devising additive noise mechanisms for differential privacy (such as Laplacian and Gaussian mechanisms), for the notion of information privacy, which accounts for prior-knowledge about the data, there are no such general purpose additive noise mechanisms. To this end, we devise a prior-aware Laplacian noise mechanism, which satisfies local information privacy. We show that adding context awareness (i.e., via the knowledge of prior of the data) improves the tradeoff between utility and privacy when compared to context-unaware mechanisms.
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