A survey of graph-modification techniques for privacy-preserving on networks.

Casas-Roma, Jordi; Herrera-Joancomartí, Jordi; Torra, Vicenç

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.

SOCIAL networks; ONLINE social network security; BIG data; INTERNET privacy; GRAPHIC methods

Artificial Intelligence Review, 2017, Vol 47, Issue 3, p341

0269-2821

Academic Journal

10.1007/s10462-016-9484-8