VLR-BPP: An intelligent virtual location replacement based bilateral privacy-preserving architecture for edge cloud systems

Abstract

Mobile Crowdsourcing (MCS) has emerged as a significant edge-cloud computing paradigm in which workers perceive data at the network edge and report it to cloud-based computing services for processing, enabling the construction of various applications. Consequently, it is imperative to achieve Bilateral Location Privacy-Preserving (BLPP) to protect the privacy of both Data Requester (DR) and workers, as disclosing location information entails many sensitive details that can result in losses for DR and workers alike. The Local Differential Privacy (LDP) approach is widely employed in Privacy-Preserving (PP) techniques due to its inherent advantages, wherein owners release data with added noise, allowing for proactive customization of privacy strength without relying on any third party. However, the current state of LDP methods presents a dilemma: when privacy protection is strong, introducing excessive location noise can lead to a decrease in the accuracy of task-worker matching, while a high rate of task-worker matching necessitates the compromise of privacy strength. In this paper, an intelligent Virtual Location Replacement based enhanced Bilateral Privacy-Preserving (VLR-BPP) architecture is proposed to improve privacy protection strength and matching accuracy in MCS simultaneously. Within the VLR-BPP architecture, a Bipartite-Graph-based Matrix Completion (BGMC) model is employed to establish the spatiotemporal correlations among data. Then, a Virtual Location Replacement (VLR) strategy is proposed to obfuscate the locations of tasks or workers to their highly correlated virtual location before publishing. Based on VLR, three preemptive location virtualization approaches are introduced: Only Task Location Virtual (OTLV), Only Workers Location Virtual (OWLV), and Both Task and Workers Location Virtual (BTWLV). For workers and DR, Randomized Response (RR) techniques and Random Matrix Multiplication Mechanism (RMM) are used to implement LDP independently. A greedy algorithm is adopted to recruit workers for tasks. In response to the data submitted by workers, BGMC imputation mechanism is utilized to enhance data quality. Finally, simulations based on real-world datasets demonstrate that the performance of our architecture surpasses existing state-of-the-art methods in privacy protection and data collection quality by 18.92∼38.17% and 15.49∼50.77%, respectively.