Privacy-Preserving Auction for Big Data Trading Using Homomorphic Encryption

Abstract

Cyber-Physical Systems (smart grid, smart transportation, smart cities, etc.), driven by advances in Internet of Things (IoT) technologies, will provide the infrastructure and integration of smart applications to accelerate the generation and collection of big data to an unprecedented scale. As a fundamental commodity in our current information age, big data is a crucial key to competitiveness in modern commerce. In this paper, we address the issue of privacy preservation for data auction in CPS by leveraging the concept of homomorphic cryptography and secure network protocol design. Specifically, we propose a generic Privacy-Preserving Auction Scheme (PPAS), in which the two independent entities of Auctioneer and Intermediate Platform comprise an untrusted third-party trading platform. Via the implementation of homomorphic encryption and one-time pad, a winner in the auction process can be determined and all bidding information is disguised. Yet, to further improve the security of the privacy-preserving auction, we additionally propose an Enhanced Privacy-Preserving Auction Scheme (EPPAS) that leverages an additional signature verification mechanism. The feasibilities of both schemes are validated through detailed theoretical analyses and extensive performance evaluations, including assessment of the resilience to attacks. In addition, we discuss some open issues and extensions relevant to our scheme.