Context-aware Data Aggregation with Localized Information Privacy
Context-aware Data Aggregation with Localized Information Privacy
复制标题
DOI：
10.1109/cns.2018.8433200
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Bo Jiang;Ming Li;R. Tandon
中科院分区：
文献类型：
--
作者：
Bo Jiang;Ming Li;R. Tandon
本文提出了局部信息隐私（LIP），作为一种新的隐私定义，它允许统计聚合，同时保护用户的隐私，而不依赖于可信的第三方。上下文感知的概念被纳入LIP的先验知识的引入，这使得设计的隐私保护的数据聚合。我们表明，LIP放松本地化差分隐私（LDP）的概念明确建模对手的知识。但是，它比2-LDP和2-相互信息隐私更严格。与其他方法相比，局部先验的结合允许LIP实现更高的效用。然后，我们提出了一个隐私保护数据聚合的优化框架，目标是最小化预期平方误差，同时满足LIP隐私约束。实用性和隐私的权衡下得到几个模型的封闭形式。然后，我们验证我们的结论，使用合成和真实世界的数据进行数值分析。结果表明，我们的LIP机制提供了更好的实用性和隐私的权衡比LDP和先验分布不均匀时，LIP的优势更加显着。
In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users’ privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion Uy explicitly modeling the adversary's knowledge. However, it is stricter than 2ϵ-LDP and ϵ-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. We then present an optimization framework for privacy-preserving data aggregation, with the goal of minimizing the expected squared error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under several models in closed-form. We then validate our conclusions by numerical analysis using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.