L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response

TL;DR

This paper introduces L-SRR, a local differential privacy framework for location-based services that employs a novel Staircase Randomized Response mechanism to enhance utility while preserving user privacy.

Contribution

The paper proposes the first LDP framework for LBS using the Staircase Randomized Response mechanism, significantly improving utility over existing LDP methods.

Findings

L-SRR outperforms other LDP schemes in practical experiments.

The mechanism achieves high utility in traffic density estimation.

Extensive experiments on real datasets validate the effectiveness of L-SRR.

Abstract

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.