Evaluating the security of anonymized big graph/structural data
Evaluating the security of anonymized big graph/structural data
复制标题
评估匿名大图/结构数据的安全性
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Ji
中科院分区：
文献类型：
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作者：
S. Ji
Shouling Ji是格鲁吉亚理工学院电气与计算机工程学院的研究员。他获得了博士学位。格鲁吉亚理工学院电气与计算机工程专业（2015年），博士在计算机科学从格鲁吉亚州立大学（2013年），和学士学位。(with荣誉）和MS。黑龙江大学计算机科学专业毕业。他目前的研究兴趣包括大数据安全和隐私，差异隐私，密码安全和数据分析。他还对图论和算法以及无线网络感兴趣。他是ACM、IEEE和IEEE COMSOC的成员，并曾担任州立大学（格鲁吉亚）IEEE学生分支的成员主席（2012-2013年）。他曾是IBM T.沃森研究中心。她是2012年中国政府优秀自费留学生奖的获得者。翻译后摘要：如今，许多计算机系统生成结构化数据（也称为图形数据）。图形数据跨越许多不同的领域，从Facebook等网络的在线社交网络数据到用于研究传染病传播的流行病学数据。图形数据定期共享用于多种目的，包括学术研究和业务合作。由于图形数据可能是敏感的，因此数据所有者经常使用各种匿名化技术，这些技术经常损害匿名化数据的结果效用。更糟糕的是，近年来有几种最先进的图形数据去匿名化攻击被证明是成功的。到目前为止，图形数据所有者无法衡量其数据的实际或理论脆弱性，也无法全面衡量其匿名化后的效用。
Shouling Ji is a Research Faculty in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He received a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2015), a Ph.D. in Computer Science from Georgia State University (2013), and B.S. (with Honors) and M.S. degrees both in Computer Science from Heilongjiang University. His current research interests include Big Data Security and Privacy, Differential Privacy, Password Security, and Data Analytics. He also has interests in Graph Theory and Algorithms, and Wireless Networks. He is a member of ACM, IEEE, and IEEE COMSOC and was the Membership Chair of the IEEE Student Branch at Georgia State University (2012-2013). He was a Research Intern at the IBM T. J. Watson Research Center. Shouling is the recipient of the 2012 Chinese Government Award for Outstanding Self-Financed Students Abroad. Abstract: Nowadays, many computer systems generate structured data (also called graph data). Graph data spans many different domains, ranging from online social network data from networks like Facebook to epidemiological data used to study the spread of infectious diseases. Graph data is shared regularly for many purposes including academic research and for business collaborations. Since graph data may be sensitive, data owners often use various anonymization techniques that often compromise the resulting utility of the anonymized data. To make matters worse, there are several state-of-the-art graph data de-anonymization attacks that have proven successful in recent years. To date, graph data owners cannot gauge the practical or theoretical vulnerability of their data, nor can they comprehensively gauge its utility after anonymization.