Participant Density-Independent Location Privacy Protection for Data Aggregation in Mobile Crowd-Sensing

Mobile crowd-sensing applications produce useful knowledge of the surrounding environment, which makes our life more predictable. However, these applications often require users to contribute, consciously or unconsciously, location-related data for analysis, which gravely encroaches users’ location privacy. Aggregate processing is a feasible way for preserving user privacy to some extent, and based on the mode, some privacy-preserving schemes have been proposed. However, existing schemes still cannot guarantee users’ location privacy in the scenarios with low density participants. Meanwhile, user accountability also needs to be considered comprehensively to protect the system against malicious users. In this paper, we propose data aggregate statistics schemes with participant density-independent location privacy-protection for mobile crowd-sensing applications. First, we make use of multi-pseudonym mechanism to overcome the vulnerability due to low participant density. Then, to further handle sybil attacks, we propose two schemes based on the Paillier cryptosystem. In the basic scheme, we leverage non-interactive zero-knowledge proof technology to verify users’ sensing data. In the advanced scheme, we present a novel verification framework, which also addresses the problem of user accountability, but at the cost of introducing a new entity. Finally, the theoretical analysis indicates that our scheme achieves the desired properties, and the performance experiments demonstrate that our scheme can achieve a balance among accuracy, privacy-protection and computational overhead.