CrowdFA: A Privacy-Preserving Mobile Crowdsensing Paradigm via Federated Analytics
Bowen Zhao, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Yingjiu Li, Robert H. Deng
Abstract
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 CROWDFA, 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, CRWODFA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWDFA, which supports various common data aggregation operations and bidding incentives. To address privacy concerns, CROWDFA 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 CROWDFA, this paper presents a privacy-preserving data aggregation scheme (PRADA) based on CROWDFA, capable of supporting a range of data aggregation operations. Additionally, a CROWDFA-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 CROWDFA protects participants’ data and bid privacy while effectively aggregating sensing data. Notably, CROWDFA outperforms state-of-the-art approaches by achieving up to 22 times faster computation time.