论文标题
L-SRR：具有楼梯随机响应的基于位置的服务的本地微分隐私
L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
论文作者
论文摘要
基于位置的服务（LB）已得到显着开发并广泛部署在移动设备中。还众所周知，LBS应用可能通过收集敏感位置引起严重的隐私问题。最近在许多不同的应用程序（例如Google Rappor，iOS和Microsoft Telemetry）中部署了强大的隐私模型“本地差异隐私”（LDP），但由于现有的LDP机制的效用较低，对LBS应用程序无效。为了解决这种缺陷，我们为各种基于位置的服务（即“ L-SRR''）提出了第一个LDP框架，该框架私下收集和分析了用高实用程序的用户位置。具体而言，我们设计了一种新型的随机机制“楼梯随机响应”（SRR），并扩展了经验估计，以显着提高不同LBS应用中SRR的效用（例如，交通密度估计和K-Neareart邻居）。我们通过在实际应用中与其他不动率方案进行基准测试，对四个真正的LBS数据集进行了广泛的实验。实验结果表明，L-SRR明显胜过它们。
Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model ''local differential privacy'' (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely ''L-SRR''), which privately collects and analyzes user locations with high utility. Specifically, we design a novel randomization mechanism ''Staircase Randomized Response'' (SRR) and extend the empirical estimation to significantly boost the utility for SRR in different LBS applications (e.g., traffic density estimation, and k-nearest neighbors). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that L-SRR significantly outperforms them.
