Privacy preservation auction in a dynamic social network. (18th October 2020)

• Record Type: Journal Article
• Authors: Hu, Xiangyu; Jin, Zhiping; Zhang, Lefeng; Zhou, Andi; Ye, Dayong
• Is Part Of: Concurrency and computation. Volume 34:Number 16(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-18
• Subjects: auction -- differential privacy -- online social network -- privacy preserving; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6058
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.