文献类型：期刊文献

英文题名：Security risk estimation of social network privacy issue

作者：Zhang, Xueqin[1]; Zhang, Li[1]; Gu, Chunhua[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2017

起止页码：81

外文期刊名：ACM International Conference Proceeding Series

收录：EI(收录号：20180704798543)

语种：英文

外文关键词：Social networking (online) - Network security

摘要：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. ? 2017 Association for Computing Machinery.