L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response
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DOI：
10.1145/3548606.3560636
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发表时间：
2022-09
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影响因子：
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通讯作者：
Han Wang;Hanbin Hong;Li Xiong;Zhan Qin;Yuan Hong
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文献类型：
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作者：
Han Wang;Hanbin Hong;Li Xiong;Zhan Qin;Yuan Hong
基于位置的服务（LBS）在移动设备中得到了长足的发展和广泛的应用。众所周知，LBS应用程序可能会因为收集敏感位置而导致严重的隐私问题。一种强大的隐私模型“本地差分隐私”（LDP）最近被部署在许多不同的应用程序中（例如b谷歌RAPPOR、iOS和Microsoft Telemetry），但由于现有LDP机制的低效用，它对LBS应用程序并不有效。为了解决这一缺陷，我们提出了第一个LDP框架，用于各种基于位置的服务（即“L-SRR”），它以高效用私下收集和分析用户位置。具体来说，我们设计了一种新的随机化机制“阶梯随机响应”（SRR），并扩展了经验估计，以显着提高SRR在不同LBS应用中的效用（例如，交通密度估计和k近邻）。我们在四个真实的LBS数据集上进行了广泛的实验，并在实际应用中与其他LDP方案进行了基准测试。实验结果表明，L-SRR显著优于它们。
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.