Participant-Density-Aware Privacy-Preserving Aggregate Statistics for 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 people to contribute, consciously or unconsciously, location-related data for analysis, and this 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 from malicious users. In this paper, we propose a participant-density-aware privacy-preserving aggregate statistics scheme for mobile crowd-sensing applications. In our scheme, we make use of multi-pseudonym mechanism to overcome the vulnerability due to low participant density. To further handle sybil attacks, based on the Paillier cryptosystem and non-interactive zero-knowledge verification, we advance and improve our solution framework, which also covers the problem of user accountability. 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.