Q-BLPP: A Quality-Enabled Bilateral Location Privacy-Preserving Service Construction Scheme in Mobile Crowd Sensing

Abstract

The widespread adoption of mobile smart devices has ushered in the era of Mobile Crowd Sensing (MCS), serving as an efficient method for large-scale data collection. Inherently location-sensitive, the service construction of MCS faces a crucial challenge of Location Privacy Preservation (LPP). Prior studies for LPP often necessitate a Trusted Third Party (TTP), which is not always feasible. Moreover, these privacy-preserving techniques may inadvertently obscure dishonest or malicious behaviors, leading to compromised Quality of Service (QoS). Motivated by this, we propose a Quality-enabled Bilateral Location Privacy-Preserving (Q-BLPP) service construction scheme, ensuring Bilateral LPP without TTP, while maintaining QoS. To achieve bilateral LPP, we introduce a novel BI-LBE algorithm using Bloom Indexing (BI) and Location-Based Encryption (LBE). Additionally, for high-quality recruitment, we present a Combinatorial Multi-Armed Bandit (CMAB) approach to balance exploration and exploitation. Furthermore, to ensure privacy during recruitment, worker profiles are anonymized using differential privacy. To our knowledge, our approach is the first to integrate QoS and LPP in MCS, with theoretical proofs of truthfulness and individual rationality. Simulations demonstrate that our Q-BLPP scheme strikes a favorable balance between computational efficiency, privacy security, and service quality, outperforming existing schemes.