Visible to the public Security Risk Estimation of Social Network Privacy Issue

TitleSecurity Risk Estimation of Social Network Privacy Issue
Publication TypeConference Paper
Year of Publication2017
AuthorsZhang, Xueqin, Zhang, Li, Gu, Chunhua
Conference NameProceeding ICCNS 2017 Proceedings of the 2017 the 7th International Conference on Communication and Network Security
Date Published2017-11-24
PublisherACM
ISBN Number978-1-4503-5349-6
KeywordsHuman Behavior, human factors, Metrics, pubcrawl, Resiliency, Scalability, Security Risk Estimation
Abstract

Users in social network are confronted with the risk of privacy leakage while sharing information with friends whose privacy protection awareness is poor. This paper proposes a security risk estimation framework of social network privacy, aiming at quantifying privacy leakage probability when information is spread to the friends of target users' friends. The privacy leakage probability in information spreading paths comprises Individual Privacy Leakage Probability (IPLP) and Relationship Privacy Leakage Probability (RPLP). IPLP is calculated based on individuals' privacy protection awareness and the trust of protecting others' privacy, while RPLP is derived from relationship strength estimation. Experiments show that the security risk estimation framework can assist users to find vulnerable friends by calculating the average and the maximum privacy leakage probability in all information spreading paths of target user in social network. Besides, three unfriending strategies are applied to decrease risk of privacy leakage and unfriending the maximum degree friend is optimal.

URLhttps://dl.acm.org/citation.cfm?id=3163073
DOI10.1145/3163058.3163073
Citation Keynoauthor_security_nodate