Abstract
Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.
Highlights
Nowadays, the ubiquity of mobile devices equipped with various functional built-in sensors and increasingly powerful wireless and 5G network has enabled the prosperity of Mobile Crowdsensing (MCS) [1] such as traffic monitoring [2] and point-of-interest characterization [3]
Paillier Encryption Data Coding (PDC) is adopted to realize the secure computation for Manhattan distance and prevent malicious users from deceiving. e main contributions of this paper are as follows: (i) We present a Location Difference-Based PrivacyPreserving Framework (LDPF) based on the powerful edge servers to solve centralization and situation of no regional differences in user location privacy-preserving
We focus on the privacypreserving of user location. e data quality of users is negatively correlated to the location, which meets the consensus of existing location-based privacy-preserving methods. erefore, our matching calculation method improves root mean squared error (RMSE) and reflects the difference between user data and task requirement data, which can be expressed as follows: matchkj−center,p−center
Summary
The ubiquity of mobile devices equipped with various functional built-in sensors (e.g., camera, microphone, and GPS) and increasingly powerful wireless and 5G network has enabled the prosperity of MCS [1] such as traffic monitoring [2] and point-of-interest characterization [3]. Ese nodes are responsible for processing data uploaded by users through mobile devices Another advantage of deploying an edge computing layer is the reduced privacy risk because these nodes can collaborate to anonymize the local data submissions without relying on a trusted and centralized sensing platform [8]. From the perspective of geo-distributed, various tasks published by the MCS platform are different, in which user location privacy-preserving should be adequately matched with regional characteristics. For popular regions, the edge layer collaborates to change location information and continuously protect participant location through CTBC without relying on a trusted sensing platform. (ii) We propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method based on the k-anonymity algorithm that can effectively guarantee location data privacy without relying on a trusted sensing platform.
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