Private rank aggregation under local differential privacy

International Journal of Intelligent SystemsVolume 35, Issue 10 p. 1492-1519 RESEARCH ARTICLE Private rank aggregation under local differential privacy Ziqi Yan, Ziqi Yan Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, ChinaSearch for more papers by this authorGang Li, Gang Li Centre for Cyber Security Research and Innovation, Deakin University, Geelong, Victoria, AustraliaSearch for more papers by this authorJiqiang Liu, Corresponding Author Jiqiang Liu jqliu@bjtu.edu.cn Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China Correspondence Jiqiang Liu, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100044 Beijing, China. Email: jqliu@bjtu.edu.cnSearch for more papers by this author Ziqi Yan, Ziqi Yan Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, ChinaSearch for more papers by this authorGang Li, Gang Li Centre for Cyber Security Research and Innovation, Deakin University, Geelong, Victoria, AustraliaSearch for more papers by this authorJiqiang Liu, Corresponding Author Jiqiang Liu jqliu@bjtu.edu.cn Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China Correspondence Jiqiang Liu, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100044 Beijing, China. Email: jqliu@bjtu.edu.cnSearch for more papers by this author First published: 22 July 2020 https://doi.org/10.1002/int.22261Citations: 7Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract In answer aggregation of crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences. Citing Literature Volume35, Issue10October 2020Pages 1492-1519 RelatedInformation