Social Networks Privacy Preserving Data Publishing
Social Networks Privacy Preserving Data Publishing
复制标题
社交网络隐私保护数据发布
DOI：
10.1109/cis.2017.00063
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Y. Challal
中科院分区：
文献类型：
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作者：
S. Bourahla;Y. Challal
社交网络的扩散允许创建大量的数据，其中包含丰富的私人信息，应该被保存。在本文中，我们认为社交网络表示为标记的二分图，其中每个节点可以有一组信息表示其配置文件。我们提出了一个解决方案，允许发布的社交网络图，同时保护数据的隐私。我们确定了一个关键的“安全分区条件”，它具有可证明的保证，以防止各种隐私攻击。我们证明了我们的解决方案的实用性，通过研究的准确性，复杂的查询可以回答匿名数据。
The proliferation of social networks allowed creating a big quantity of data which contains rich private information that should be preserved. In this paper we consider social networks that are represented as labeled bipartite graphs where each node can have a set of information representing its profile. We propose a solution that allows publishing the social network graphs while preserving the privacy of data. We identify a critical "safety partitioning condition" which has provable guarantees to prevent variety of privacy attacks. We demonstrate the utility of our solution by studying the accuracy with which complex queries can be answered over the anonymized data.
DOI：
--
发表时间：
2015
期刊：
Advances in neural information processing systems
影响因子：
--
作者：
Borgs, Christian;Chayes, Jennifer;Smith, Adam
通讯作者：
Smith, Adam