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基于半监督学习的社交网络用户属性预测
引用本文:丁宇新,肖 骁,吴美晶,张逸彬,董 丽. 基于半监督学习的社交网络用户属性预测[J]. 通信学报, 2014, 35(8): 15-22. DOI: 10.3969/j.issn.1000-436x.2014.08.004
作者姓名:丁宇新  肖 骁  吴美晶  张逸彬  董 丽
作者单位:1.哈尔滨工业大学 深圳研究生院,广东 深圳 518055;2. 中国科学院计算所 计算机体系结构国家重点实验室,北京 100190
基金项目:国家自然科学基金资助项目(61100192);中国科学院计算所计算机体系结构国家重点实验室开放基金资助项目;哈尔滨工业大学科研创新基金资助项目(2010123);哈尔滨工业大学深圳研究生院网络智能计算重点实验室基金资助项目
摘    要:研究如何利用社交关系推测用户的隐藏属性(私隐信息),采用基于图的半监督学习方法推测用户属性。为了提高预测的准确率,提出利用属性聚集度评价属性推测的难易程度,并依用户节点标记的不同,设计不同的权重公式计算用户之间的关系强度。以“人人网”数据作为实验数据,对用户的兴趣与毕业学校进行预测,验证了方法的有效性。

关 键 词:社交网络;属性推测;半监督学习;信息安全

Privacy-perserving scheme for social networks
Zhi-quan LV,Cheng HONG,Min ZHANG,Deng-guo FENG,Kai-qu CHEN. Privacy-perserving scheme for social networks[J]. Journal on Communications, 2014, 35(8): 15-22. DOI: 10.3969/j.issn.1000-436x.2014.08.004
Authors:Zhi-quan LV  Cheng HONG  Min ZHANG  Deng-guo FENG  Kai-qu CHEN
Affiliation:1. TCA Institute of Software,Chinese Academy of Sciences,Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. National Supercomputing Center in Shenzhen, Shenzhen 518055, China
Abstract:The security and privacy issues in SNS were studied and a privacy-preserving scheme PPSNS was proposed. PPSNS utilizes attribute-based encryption, allowing the SNS user to set up an enforcement of fine-grained access control upon the data he owns, thus the potential threats from unauthorized parties or even the SNS provider itself could be avoided. A token system in PPSNS is included to address the challenging issue of efficient attribute revocation. In addi-tion, the users in PPSNS don't have to manage as much information as they do in other encryption-based solutions, achieving a much lower cost in the client side. Analyses show that PPSNS is secure, and gives a better performance in computing and storage costs compared to most related works.
Keywords:social network   inferring profiles   semi-supervised learning   information security
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