A survey of graph-modification techniques for privacy-preserving on networks
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- 作者：Jordi Casas-Roma ; Jordi Herrera-Joancomartí ; Vicenç Torra
- 关键词：Privacy ; k ; Anonymity ; Randomization ; Social networks ; Graphs
- 刊名：Artificial Intelligence Review
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：47
- 期：3
- 页码：341-366
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence (incl. Robotics); Computer Science, general;
- 出版者：Springer Netherlands
- ISSN：1573-7462
- 卷排序：47
文摘
Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.