Abstract
With the enhancement of the positioning function of mobile devices and the upgrade of communication networks, location-based service (LBS) has become an important application of mobile devices. Among the numerous researches on location privacy preservation, cloud-based location privacy preservation has become a hot topic, but it undoubtedly brings new problems such as data confidentiality and user privacy disclosure. This paper proposes an accountable outsourced LBS privacy-preserving scheme. In the outsourcing scenario, in order to make users interact with cloud server to obtain query data, firstly we construct location hierarchical index and attribute hierarchical index based on Bloom Filter, and secondly we divide one region into atomic regions using Hilbert Curve, both of which ensure the privacy of query and improve the efficiency of query. At last, we realize the sharing of encrypted data among different users by accountable proxy re-encryption (APRE) technology, which can effectively suppress the abuse of proxy re-encryption key. We demonstrate the correctness of the proposed scheme through security analysis, and show the effectiveness of the scheme through performance analysis.
Highlights
With the development of wireless communication technology and mobile positioning technology, more and more mobile devices have the precise positioning function of GPS, which makes the location-based service (LBS) increasingly popular
cloud service provider (CSP) can lighten the burden of LBSP, it poses a number of threats
LEAKAGE FUNCTION The leak function represents all the information that the opponent can obtain throughout the system. It includes the query area qvR sent by the user to the LBSP, region division {ARi}(1 i n) sent by LBSP to users, query q sent by user to the CSP, the encrypted data, attribute index qvattr sent by LBSP to CSP and so on
Summary
With the development of wireless communication technology and mobile positioning technology, more and more mobile devices have the precise positioning function of GPS, which makes the location-based service (LBS) increasingly popular. Liu et al.: Accountable Outsourcing Location-Based Services With Privacy Preservation in order to lighten its burden This is clearly not what users and LBSP want to see. (1) We construct a LBS privacy-preserving scheme for query outsourcing, and generate the location index and attribute index using Bloom Filter with key-hash function. Reference [43] proposes spatial query on outsourced private data and password conversion scheme based on Hilbert curve, which protects the privacy of spatial data against all kinds of attacks and is robust to the attack model. Liang et al [44] combine K anonymity with Hilbert curve and propose a privacy-preserving method for the query of POIs in the road network environment, which can effectively resist the inference attacks. PRELIMINARIES This section provides a brief overview of the fundamental techniques used in the proposed scheme
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