protect you more than blank：anti-learning sensitive user information in the social networks.pdfVIP

Yuan M, Chen L, Yu PS et al. Protect you more than blank: Anti-learning sensitive user information in the social networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 29(5): 762–776 Sept. 2014. DOI 10.1007/s11390-014-1466-1 Protect You More Than Blank: Anti-Learning Sensitive User Information in the Social Networks 1,2 1 3 Mingxuan Yuan ( ²Z ), Lei Chen ( X), Member, IEEE, Philip S. Yu , Fellow, ACM, IEEE and Hong Mei4,5,6 (r ÷ ), Fellow, CCF, Member, ACM, IEEE 1Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong China 2Huawei Noah’s Ark Laboratory, Shatin, Hong Kong, China 3Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60637, U.S.A. 4Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China 5Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China 6Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China E-mail: yuan.mingxuan@; leichen@cse.ust.hk; psyu@; meih@ Received April 7, 2014; revised July 4, 2014. Abstract Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high ac