Optimizing Location Quality in Privacy Preserving Crowdsensing

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations。 Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.