DNF-BLPP: An Effective Deep Neuro-Fuzzy Based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal Crowdsourcing
Zihui Sun, Anfeng Liu, Naixue N. Xiong, Shaobo Zhang, Tian Wang · IEEE Transactions on Services Computing · 2024
Mobile Crowd Sensing (MCS) is an emerging paradigm that constructs various services by recruiting massive workers in edge networks to sense data. However, ensuring Quality-of-Service (QoS) while preserving bilateral location privacy remains a critical challenge for effective service provisioning in the context of spatiotemporal crowdsourcing. Previous studies have achieved privacy preservation through location obfuscation, which has the problem of low task-worker matching rate, and data loss adversely affected QoS. To tackle this issue, we propose a Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving (DNF-BLPP) scheme to construct services in spatiotemporal crowdsourcing. In this article, we first present a novel obfuscation strategy that obfuscates the location of each task and worker respectively to${\rm{\lambda }}$locations with the highest correlation, ensuring privacy preservation while providing assurance for accurate data recovery. Then, we further introduce a deep neural-fuzzy approach to solve the worker selection problem under obfuscated locations. Based on that, a Non-negative Constraint Matrix Factorization algorithm is employed to accurately impute missing data based on time-space correlation. Theoretical analysis and extensive simulations show that the proposed scheme has a strong ability to protect location privacy, and is better than the state-of-the-art schemes in performance indicators such as task-worker matching rate, QoS and computational efficiency.