Behavioral-Semantic Privacy Protection for Continual Social Mobility in Mobile-Internet Services

Crowdsensing-based mobile Internet, while facilitating users' daily life, also raises privacy concerns because of sharing user location trajectories. Combining with open-source network information, these trajectories reveal the semantics of users' social behaviors in their travels, thus indicating their behavioral traces. Based on such social mobilities, attackers can explore users' potential behavioral patterns and launch powerful behavioral-semantic inferential attacks for behavior prediction, identity identification, and threatening users' location-related mobile privacy. Even through privacy protection, the released similar anonymous semantics may still bring significant privacy gains to such attacks. To the best of our knowledge, there is still no effective technique to counter such attacks and protect user behavioral semantics in mobile Internet services. To this end, this article proposes a posterior behavioral-semantic privacy-preserving solution, BSPri, by simulating the inferential attacks to eliminate the privacy risks associated with released traces. Specifically, we represent the logical association between semantic attributes and propose a similar semantic clustering and ranking method. Then, we formalize the user social-mobility stochastic process to characterize the privacy risks arising from the attacker's observation of the released trajectory, and define a observation-based posterior privacy authentication criteria to filter anonymous semantics further. Finally, we generate synthetic trajectories with similar anonymity semantics, which bring attackers insignificant privacy gain, for users to participate in applications. Extensive experiments with the real world data set demonstrate that our BSPri achieves an effective privacy-preserving performance, i.e., rigorous posterior-privacy constraint with limited data-availability loss, such as, distance 752 m (47-m closer, compared with our previous work MSP), direction deviation 39.5 degrees (11.5 degrees smaller), and semantic similarity 43.4% (8.4% closer).