Crowdsensing Data Trading for Unknown Market: Privacy, Stability, and Conflicts

In recent years, Crowdsensing Data Trading (CDT) has emerged as a new data trading paradigm, where buyers crowdsource data collection tasks to a group of mobile users with sensing devices (a.k.a., sellers) who sell the collected data to them, through a platform as the broker for a long-term data trading. One of the most critical issues in CDT is ensuring the stability of the matching between buyers and sellers in the data trading market. In this paper, we focus on privacy protection and the stability problems in the CDT market with unknown preference sequences of buyers. The goal is to protect sellers’ data qualities and ensure the CDT market's stability while maximizing the cumulative data quality for each task. We model this problem as a differentially private multi-player multi-armed competing bandit problem and propose a novel metric of the approximate stability, called $\delta$-stability. We propose a privacy-preserving stable CDT mechanism called DPS-CB to solve this problem in the centralized setting, which is based on stable matching theory, and competing bandit strategy. Moreover, we extend it into decentralized setting in order to avoid the competitive matching conflicts caused in this setting and propose a Conflicts-avoiding DPS-CB mechanism, called CDPS-CB, by using Bernoulli probability and selecting feasible sets of sellers. In addition, we prove the security and stability of the CDT market under privacy concerns and analyze the regret performance of DPS-CB and CDPS-CB mechanisms, respectively. Finally, the significant performance of these two mechanisms is demonstrated through extensive simulations on a real-world dataset.