CrowdFA: A privacy-preserving mobile crowdsensing paradigm via federated analytics

• Main Authors: ZHAO, Bowen, LI, Xiaoguo, LIU, Ximeng, PEI, Qingqi, LI, Yingjiu, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Crowdsensing;privacy protection;data aggre-gation;reward distribution;federated analytics; Information Security; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8226; https://ink.library.smu.edu.sg/context/sis_research/article/9229/viewcontent/CrowdFA_av.pdf
• Institution: Singapore Management University

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution encompassing data aggregation, incentive design, and privacy protection. Specifically, inspired by FA, CROWD FA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWD FA, which supports various common data aggregation operations and bidding incentives. To address privacy concerns, CROWD FA relies solely on an efficient cryptographic primitive known as additive secret sharing to simultaneously achieve privacy-preserving data aggregation and privacy-preserving incentive. To instantiate CROWD FA, this paper presents a privacy-preserving data aggregation scheme (PRADA) based on CROWD FA, capable of supporting a range of data aggregation operations. Additionally, a CROWD FA-based privacy-preserving incentive mechanism (PRAED) is designed to ensure truthful and fair incentives for each participant, while maximizing their individual rewards. Theoretical analysis and experimental evaluations demonstrate that CROWD FA protects participants' data and bid privacy while effectively aggregating sensing data. Notably, CROWD FA outperforms state-of-the-art approaches by achieving up to 22 times faster computation time.