The privacy preserving auction mechanisms in IoT-based trading market: A survey

Nowadays, the Internet of Things (IoT) technology has greatly promoted the transaction behavior. It has led to a shift from the traditional face-to-face, bartering business to today’s online virtual goods business, such as energy trading, spectrum, and so on. However, there exist two critical challenges in IoT-based trading market, which include “efficiency” and “safety”. “Efficiency” indicates a fair-trading decision-making rule, and “safety” indicates that the bidders’ privacy must be protected. To address the above challenges, numerous researchers are working on privacy-preserving auction mechanisms for IoT-based transactional markets, drawing insights from electronic auction theory and information security theory. However, a predominant focus on specific scenarios characterizes most existing works, lacking a comprehensive synthesis of the collective contributions to date. Therefore, this paper systematically elucidates privacy-preserving auction mechanisms for IoT-based transactional markets. Firstly, we expound the foundational knowledge of IoT-based auction markets, delving into auction theory and privacy theory through concise categorization. Secondly, we address both theoretical considerations (from the integration perspective of various privacy protection methods and auction mechanisms) and practical aspects (evaluating various real-world application scenarios). Each aspect undergoes meticulous scrutiny, providing practical assessments and prospects. Lastly, we propose future research directions. Key challenges include the absence of inference attack models for differential privacy, a dearth of algorithmic designs with privacy-preserving capabilities at the auction mechanism level, and the intricate balance between privacy and efficiency. Proposed solutions for future research directions include leveraging Bayesian inference and neural networks for effective attacks, designing autonomous privacy protection mechanisms, and addressing the privacy-efficiency trade-off through the application of Markov decision processes.