Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation
Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation
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DOI：
10.1109/tdsc.2020.3041733
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发表时间：
2022-05-01
影响因子：
7.3
通讯作者：
Tandon, Ravi
中科院分区：
文献类型：
--
作者：
Jiang, Bo;Li, Ming;Tandon, Ravi
在这篇文章中，我们提出了本地信息隐私（LIP），并设计了基于LIP的统计聚合机制，同时保护用户的隐私，而不依赖于可信的第三方。LIP引入了上下文感知的概念，它可以被看作是利用数据的先验（包括私有化和后处理）来提高数据的效用。我们提出了一个优化框架，以最大限度地减少数据聚合的均方误差，同时保护每个用户的输入数据或相关的潜在变量的隐私，通过满足LIP约束。然后，我们研究了在不同的情况下，考虑先验的不确定性和相关性的潜变量的最优机制。在这篇文章中，包括随机响应（RR），一元编码（UE），和本地哈希（LH）的三种类型的机制进行了研究，我们推导出封闭形式的解决方案的最佳扰动参数是先验相关的。我们比较了基于LIP的机制和基于LDP的机制，并从理论上证明了前者实现了增强的效用。然后，我们研究两个应用程序：（加权）求和和直方图估计，并显示如何提出的机制可以应用到每个应用程序。最后，我们验证了我们的分析模拟使用合成和真实世界的数据。结果显示了不同的先验分布，相关性和输入域大小对数据效用的影响。结果还表明，我们的LIP为基础的机制提供了更好的实用性，隐私权衡比基于LDP的。
In this article, we propose local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The concept of context-awareness is incorporated in LIP, which can be viewed as exploiting of data prior (both in privatizing and post-processing) to enhance data utility. We present an optimization framework to minimize the mean square error of data aggregation while protecting the privacy of each user's input data or a correlated latent variable by satisfying LIP constraints. Then, we study optimal mechanisms under different scenarios considering the prior uncertainty and correlation with a latent variable. Three types of mechanisms are studied in this article, including randomized response (RR), unary encoding (UE), and local hashing (LH), and we derive closed-form solutions for the optimal perturbation parameters that are prior-dependent. We compare LIP-based mechanisms with those based on LDP, and theoretically show that the former achieve enhanced utility. We then study two applications: (weighted) summation and histogram estimation, and show how proposed mechanisms can be applied to each application. Finally, we validate our analysis by simulations using both synthetic and real-world data. Results show the impact on data utility by different prior distributions, correlations, and input domain sizes. Results also show that our LIP-based mechanisms provide better utility-privacy tradeoffs than LDP-based ones.