Secure Friend Discovery via Privacy-Preserving and Decentralized Community Detection

Autor:	Hu, Pili, Chow, Sherman S. M., Lau, Wing Cheong
Rok vydání:	2014
Předmět:	Computer Science - Cryptography and Security Computer Science - Social and Information Networks Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery. We formulate the first privacy-preserving and decentralized community detection problem as a multi-objective optimization. We design the first protocol to solve this problem, which transforms community detection to a series of Private Set Intersection (PSI) instances using Truncated Random Walk (TRW). Preliminary theoretical results show that our protocol can uncover communities with overwhelming probability and preserve privacy. We also discuss future works, potential extensions and variations. Comment: ICML 2014 Workshop on Learning, Security and Privacy
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1405.4951 Zobrazit plný text záznamu View this record from Arxiv