DPDT: A Differentially Private Crowd-Sensed Data Trading Mechanism

Abstract

Along with the generation of Internet of Things (IoT), the values of tremendous volumes of sensing data will be slowly unlocked. Thus, crowd-sensed data trading as a new business paradigm has recently attracted increasing attention. A typical data trading system contains a platform, data consumers, and crowd workers. The platform recruits crowd workers to collect data and then sells the data to consumers. In this article, we design a differentially private crowd-sensed data trading mechanism, called DPDT, to preserve the identity privacy of consumers and the task privacy against crowd workers during the data collection process, simultaneously. DPDT consists of a differentially private auction-based data pricing algorithm and a differentially private data collection algorithm. The data pricing algorithm achieves a good approximation to the maximum revenue. Meanwhile, it guarantees $(e^{2}-1)\epsilon $ -truthfulness and $2\epsilon $ -differential privacy, where $\epsilon >0$ is a small constant. The data collection algorithm is able to effectively protect the data collection task privacy against crowd workers. We prove that this data collection algorithm achieves $\delta $ -approximate $\epsilon $ -differential privacy, where $\delta is a small constant, and meanwhile guarantees a tight bound of the expected approximation ratio. At last, extensive simulations are conducted to verify the significant performance of DPDT.