Privacy-preserving Cooperative Online Matching over Spatial Crowdsourcing Platforms

Yi Yang, Yurong Cheng*, Ye Yuan, Guoren Wang, Lei Chen, Yongjiao Sun

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform’s revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.

Original languageEnglish
Pages (from-to)51-63
Number of pages13
JournalProceedings of the VLDB Endowment
Volume16
Issue number1
DOIs
Publication statusPublished - 2022
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sept 2023

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