A Contract-Based Privacy-Preserving Longitudinal Data Trading Mechanism for IoT

Internet of Things (IoT) devices generate vast amounts of real-time data across diverse sectors, offering lucrative opportunities in the data trading market. This facilitates the conversion of raw data into valuable products and services, resulting in significant economic and social benefits. To issue data security problems in trading mechanisms, several solutions based on local differential privacy (LDP) provide lightweight methods for privacy preservation and efficient data exchange. However, most solutions lack effective mechanisms for longitudinal data. Moreover, LDP needs to address the data quality problem in IoT. At last, the pricing of privacy-preserving longitudinal data remains unresolved. To address these problems, we propose a privacy-preserving data trading (PPDT) scheme for IoT in this article. Specifically, to guarantee the security of longitudinal data, we utilize two perturbation techniques to accommodate data owners with varying privacy preferences. To enhance data availability, we devise a binary tree-based aggregation algorithm combined with a weighted average strategy and maximum likelihood estimation. Additionally, we derive an optimal contract that considers different levels of privacy preservation and data trading prices. In scenarios with incomplete information, the contract can provide appropriate incentives to the involved parties. Finally, we demonstrate the efficiency and effectiveness of the proposed data trading scheme through theoretical analysis and extensive experiments.