SSPA-LBS: Scalable and Social-Friendly Privacy-Aware Location-Based Services

Abstract

Privacy-aware location-based service (PA-LBS) preserves LBS users’ privacy but undesirably sacrifices service quality. In order to balance the two factors with satisfactory user experience, existing frameworks are faced with two barriers, that is, scalability and social-friendliness. First, existing schemes do not enable LBS users to flexibly scale their privacy level on service provision. Such a lack of scalability easily results in either unacceptable service-quality degradation or insufficient privacy protection and fails to meet dynamic user requirements. Second, existing schemes handle privacy protection by merely considering the trust relationship between users and servers but ignore the complex trust relationships among users. As a result, users cannot preserve privacy in location-based social services that involve user-to-user interactions. In this paper, we present the first scalable and social-friendly PA-LBS system. In particular, we propose a novel camouflage algorithm with a formal privacy guarantee that enables LBS users to expose their location information by scaling two privacy related factors, that is, camouflage range and place type. Furthermore, we apply the scalable ciphertext policy attribute-based encryption algorithm to enable LBS users to effectively control the access from other users to their location information. Moreover, we also demonstrated the operational efficiency of the proposed system through successful implementations on Android devices.