Context-Aware Local Information Privacy
Context-Aware Local Information Privacy
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DOI：
10.1109/tifs.2021.3087350
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发表时间：
2021-01-01
影响因子：
6.8
通讯作者：
Li, Ming
中科院分区：
文献类型：
--
作者：
Jiang, Bo;Seif, Mohamed;Li, Ming
本文对本地信息隐私(LIP)进行了研究。作为一种上下文感知隐私概念，LIP通过引入先验知识来放松本地差异隐私(LDP)的事实上的标准隐私概念，从而获得更好的实用性。我们研究了LIP与一些有代表性的隐私概念的关系，包括LDP、互信息和最大泄漏。我们表明，与其他上下文感知隐私概念相比，LIP提供了强大的实例隐私保护。此外，我们还介绍了LIP的一些有用的性质，包括后处理、连接性、可组合性、可转移性和对不完全先验知识的鲁棒性。然后，我们研究了一个通用的效用-隐私折衷框架，在该框架下，我们推导出了基于LIP的离散值和连续值数据的隐私保护机制。本文研究了三种扰动机制：1)随机响应(RR)，2)随机采样(RS)和3)加性噪声(AN)(如高斯机制)。我们的隐私机制将先验知识融入到扰动参数中，以增强实用性。最后，我们在真实数据集上进行了一组全面的实验，以说明上下文感知的优势，并比较了不同机制提供的效用和隐私之间的权衡。
In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.