Nowadays, location-based services (LBS) are facilitating people in daily life through answering LBS queries. However, privacy issues including location privacy and query privacy arise at the same time. Existing works for protecting query privacy either work on trusted servers or fail to provide sufficient privacy guarantee. This paper combines the concepts of differential privacy and k-anonymity to propose the notion of differentially private k-anonymity (DPkA) for query privacy in LBS. We recognize the sufficient and necessary condition for the availability of 0-DPkA and present how to achieve it. For cases where 0-DPkA is not achievable, we propose an algorithm to achieve 𝜖-DPkA with minimized 𝜖. Extensive simulations are conducted to validate the proposed mechanisms based on real-life datasets and synthetic data distributions.
Personal and Ubiquitous Computing – Springer Journals
Published: Mar 9, 2018
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