Discovering Truth in Mobile Crowdsensing with Differential Location Privacy

The combination of Mobile Crowdsensing (MCS) and truth discovery has benefited the ubiquitous monitoring and analysis of the physical world. To address the concerns alongside user data collection, the literature has partially studied data privacy protection for truth discovery. Yet, the threats of location leakage remain overlooked in such contexts. For joint accommodating privacy protection and truth elaboration, we propose to leverage differential privacy for distributed user location obfuscation and explore spatial correlation for corresponding observation's value calibration. We form this process into a truth estimation deviation minimization problem under differential privacy and obfuscation requirements. By theoretically transforming it into probabilistic calibration residual optimization, the problem can be solved via linear programming. Evaluation on real-world temperature and humid sensing data shows its effectiveness on providing significant location distortion distance and practically acceptable time consumption. Results also reveal an up to 53% truth discovery accuracy improvement compared to the SCP baseline.