anonymizing Social Networks
anonymizing Social Networks
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DOI：
10.1201/9781420091502-26
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Arvind Narayanan;Vitaly Shmatikov
中科院分区：
文献类型：
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作者：
Arvind Narayanan;Vitaly Shmatikov
在线社交网络运营商越来越多地分享用户及其与广告商、应用程序开发人员和数据挖掘研究人员之间关系的潜在敏感信息。隐私通常通过匿名化来保护，即删除姓名、地址等。我们提出了一种分析社交网络中的隐私和匿名性的框架，并开发了一种针对匿名社交网络图的新的再识别算法。为了证明它在现实世界网络中的有效性，我们展示了三分之一的用户可以被验证同时拥有Twitter（一个流行的微博服务）和Flickr（一个在线照片分享网站）的账户，可以在匿名Twitter图中重新识别，错误率只有12%。我们的去匿名化算法纯粹基于网络拓扑，不需要创建大量虚拟“符号”节点，对噪声和所有现有防御都具有鲁棒性，即使在目标网络和对手的辅助信息重叠时也能工作
Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application develo pers, and data-mining researchers. Privacy is typically protect ed by anonymization, i.e., removing names, addresses, etc. We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized socialnetwork graphs. To demonstrate its effectiveness on realworld networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharin g site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary informati on