Bayesian-Based Inference Attack Method and Individual Differential Privacy-Based Auction Mechanism for Double Auction Market

Abstract

Due to high convenience and efficiency, electronic auction technology has been developed rapidly and has been applied to many online trading market applications. As more attention has been paid to information security, the privacy issues in the electronic auction have been widely studied. Differential privacy, as a lightweight privacy protection method, is an important direction in privacy preserving auction mechanism designing. However, most of the existing researches on differential privacy-based auction mechanism have not proposed a theoretical privacy inference attack method against the auction market. Therefore, the existence of privacy attacks is questionable, and the necessity and privacy protection performance of the existing differential privacy auction mechanism cannot be verified. To this end, in this paper we addressed the privacy attack issue and privacy protection issue in the auction market simultaneously. First, a Bayesian-based inference attack method against the double auction market was proposed from the perspective of the adversary. Theoretical analysis and evaluation results showed that the proposed inference attack method can effectively infer the bidding information of the target bidders, and attack success rate achieved approximately 95%. Second, an individual differential privacy-based auction mechanism was proposed from the perspective of the auction platform. Since not all the bidders will be attacked, we introduced the concept of individual differential privacy to provide targeted defense for specific bidders. Theoretical analysis demonstrated that the proposed auction mechanism satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\varepsilon $ </tex-math></inline-formula> -individual differential privacy. And the extensive evaluation results showed that, compared with the existing differential privacy-based auction mechanism, our proposed mechanism provided the best privacy protection performance, that is, reduced the attack success rate to 20%, and ensured better auction performance, such as social welfare and satisfaction ratio, than the other mechanisms. Note to Practitioners—In this paper, we addressed the non-invasive privacy issues in the widely used electronic auction mechanism. Most of the previous works focused on designing differential privacy based auction mechanism against the non-invasive privacy attack, but neglecting the principle of non-invasive privacy attack methods. This makes it impossible to verify the privacy protection effectiveness of their proposed mechanisms. For this reason, a very large privacy budget may be selected to ensure the efficiency of privacy protection, which will lead to poor auction performance. To this end, we first proposed a Bayesian-based inference attack method against the double auction market. The proposed inference attack method allows the adversary infer the bidders’ private bidding information by the public auction results. Moreover, we then proposed an individual differential privacy auction mechanism, which aimed to achieve effective privacy protection while minimizing the added noise, thereby improving auction performance. The experiments demonstrate that the proposed Bayesian-based inference attack method achieves a good attack successful rate, and the proposed individual differential privacy auction mechanism will achieve the better efficiency of privacy protection as well as auction performance comparing with the exist differential privacy-based auction mechanism. In conclusion, this paper provides a verification method for the future research on the privacy protection of electronic auction mechanism. Meanwhile, this paper proposes an efficient privacy protection auction mechanism, which can be used in various trading scenarios.