DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

Since smartphones embedded with positioning systems and digital maps are widely used, location-based services (LBSs) are rapidly growing in popularity and providing unprecedented convenience in people's daily lives; however, they also cause great concern about privacy leakage. In particular, location queries can be used to infer users' sensitive private information, such as home addresses, places of work and appointment locations. Hence, many schemes providing query anonymity have been proposed, but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS. To address this challenge, a novel dual privacy-preserving scheme (DPPS) is proposed that includes two privacy protection mechanisms. First, to prevent privacy disclosure caused by correlations between locations, a correlation model is proposed based on a hidden Markov model (HMM) to simulate users' mobility and the adversary's prediction probability. Second, to provide query probability anonymity of each single location, an advanced k-anonymity algorithm is proposed to construct cloaking regions, in which realistic and indistinguishable dummy locations are generated. To validate the effectiveness and efficiency of DPPS, theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft, i.e., GeoLife dataset.

Automated summary

With the widespread use of smartphones with embedded positioning systems and digital maps, location-based services (LBSs) have become popular and convenient in people's daily lives, but concerns about privacy leakage have emerged. To address this issue, a dual privacy-preserving scheme (DPPS) is proposed, which includes a correlation model based on a hidden Markov model (HMM) to prevent privacy disclosure caused by location correlations, and an advanced k-anonymity algorithm to provide query probability anonymity for each single location by constructing cloaking regions with realistic and indistinguishable dummy locations. The effectiveness and efficiency of DPPS are validated through theoretical analysis and experimental verification using a real-life dataset.