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Location-based services (LBS) have been significantly developed and widelydeployed in mobile devices. It is also well-known that LBS applications mayresult in severe privacy concerns by collecting sensitive locations. A strongprivacy model ''local differential privacy'' (LDP) has been recently deployedin many different applications (e.g., Google RAPPOR, iOS, and MicrosoftTelemetry) but not effective for LBS applications due to the low utility ofexisting LDP mechanisms. To address such deficiency, we propose the first LDPframework for a variety of location-based services (namely ''L-SRR''), whichprivately 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 utilityfor SRR in different LBS applications (e.g., traffic density estimation, andk-nearest neighbors). We have conducted extensive experiments on four real LBSdatasets by benchmarking with other LDP schemes in practical applications. Theexperimental results demonstrate that L-SRR significantly outperforms them.

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