Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143529
Title: Privacy-preserving user profile matching in social networks
Authors: Yi, Xun
Bertino, Elisa
Rao, Fang-Yu
Lam, Kwok-Yan
Nepal, Surya
Bouguettaya, Athman
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Yi, X., Bertino, E., Rao, F.-Y., Lam, K.-Y., Nepal, S., & Bouguettaya, A. (2019). Privacy-preserving user profile matching in social networks. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1572-1585. doi:10.1109/TKDE.2019.2912748
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.
URI: https://hdl.handle.net/10356/143529
ISSN: 1041-4347
DOI: 10.1109/TKDE.2019.2912748
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2019.2912748.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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