PrivLBS : Local Differential Privacy for Location-Based Services with Staircase Randomized Response
PrivLBS : Local Differential Privacy for Location-Based Services with Staircase Randomized Response
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2022
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基于位置的服务（LBS）已经得到显著发展并且广泛部署在移动的设备中。众所周知，LBS应用程序可能会收集敏感位置，从而导致严重的隐私问题。强隐私模型“局部差分隐私”（LDP）最近已经被部署在许多不同的应用中（例如，Google RAPPOR、iOS和Microsoft Telemetry），但由于现有LDP机制的低实用性，对LBS应用程序无效。为了解决这样的缺陷，我们提出了第一个LDP框架的各种基于位置的服务（即“PrivLBS”），私人收集和分析用户的位置与高效用。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，以显着提高不同LBS应用中PrivLBS的效用（例如，交通密度估计，以及k个最近的兴趣点）。我们在四个真实的LBS数据集上进行了广泛的实验，并与实际应用中的其他LDP方案进行了比较。实验结果表明，PrivLBS的性能明显优于它们。
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 “ PrivLBS ”), 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 PrivLBS in different LBS applications (e.g., traffic density estimation, and k nearest POIs). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that PrivLBS significantly outperforms them.