Locally Differentially Private Protocols for Frequency Estimation
Locally Differentially Private Protocols for Frequency Estimation
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发表时间：
2017-08
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通讯作者：
Tianhao Wang;Jeremiah Blocki;Ninghui Li;S. Jha
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作者：
Tianhao Wang;Jeremiah Blocki;Ninghui Li;S. Jha
满足当地差异性隐私（LDP）的协议使各方能够在不依赖于可信赖的第三方协议（例如Google的拉波特）中收集有关人口的汇总信息协议，用户对其私人信息进行编码，并在将其发送给当地的编码值之前，然后将其发送给聚合器，该值结合了用户为用户贡献的值在本文中，我们提出了一个框架，该框架通常在文献中提出了一些简单的协议。最佳参数，产生两个新协议（即优化的一单位编码和优化的本地哈希），它们提供了比协议更好的实用性先前提出的。我们在使用每个提议的方案时提出了精确条件，并执行实验，以证明我们提出的协议的优势。
Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate information about a population while protecting each user’s privacy, without relying on a trusted third party. LDP protocols (such as Google’s RAPPOR) have been deployed in real-world scenarios. In these protocols, a user encodes his private information and perturbs the encoded value locally before sending it to an aggregator, who combines values that users contribute to infer statistics about the population. In this paper, we introduce a framework that generalizes several LDP protocols proposed in the literature. Our framework yields a simple and fast aggregation algorithm, whose accuracy can be precisely analyzed. Our in-depth analysis enables us to choose optimal parameters, resulting in two new protocols (i.e., Optimized Unary Encoding and Optimized Local Hashing) that provide better utility than protocols previously proposed. We present precise conditions for when each proposed protocol should be used, and perform experiments that demonstrate the advantage of our proposed protocols.