Privacy-preserving Stable Crowdsensing Data Trading for Unknown Market

As a new paradigm of data trading, Crowdsensing Data Trading (CDT) has attracted widespread attention in recent years, where data collection tasks of buyers are crowdsourced to a group of mobile users as sellers through a platform as a broker for long-term data trading. The stability of the matching between buyers and sellers in the data trading market is one of the most important CDT issues. In this paper, we focus on the privacy-preserving stable CDT issue with unknown preference sequences of buyers. Our goal is to maximize the accumulative data quality for each task while protecting the data qualities of sellers and ensuring the stability of the CDT market. We model such privacy-preserving stable CDT issue with unknown preference sequences as a differentially private competing multi-player multi-armed bandit problem. We define a novel metric δ-stability and propose a privacy-preserving stable CDT mechanism based on differential privacy, stable matching theory, and competing bandit strategy, called DPS-CB, to solve this problem. Finally, we prove the security and the stability of the CDT market under the effect of privacy concerns and analyze the regret performance of DPS-CB. Also, the performance is demonstrated on a real-world dataset.