Privacy-aware Data Trading in Smart Cities
PI: Dr Xianhao CHEN
Abstract:
Data is the lifeblood of smart cities. However, the collection or sharing of personal data faces significant challenges due to privacy concerns and a lack of incentives. As a result, there is a pressing need to establish an automated data trading market enabling data owners to sell data while maintaining control over potential privacy breaches.
By leveraging advancements in financial technology, such as auction and blockchain mechanisms, our objective is to develop privacy-aware auction systems that encourage data owners to sell their data with customized privacy guarantees. Specifically, we aim to design privacy-aware auction mechanisms for both platform-based data trading and peer-to-peer data trading. In platform-based trading scenarios, we will develop incentive-compatible reverse auction mechanisms. For peer-to-peer trading, we plan to utilize blockchain technology for decentralized implementations, thereby guaranteeing secure and transparent trading without needing a trusted platform. Additionally, we will extend these scenarios to include collaborative machine learning by motivating data owners to participate in model training for intelligence extraction.