A survey of graph-modification techniques for privacy-preserving on networks
2017 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 47, no 3, 341-366 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Springer, 2017. Vol. 47, no 3, 341-366 p.
Privacy, k-Anonymity, Randomization, Social networks, Graphs
Research subject Skövde Artificial Intelligence Lab (SAIL)
IdentifiersURN: urn:nbn:se:his:diva-13465DOI: 10.1007/s10462-016-9484-8ISI: 000394302100003ScopusID: 2-s2.0-84973106733OAI: oai:DiVA.org:his-13465DiVA: diva2:1086165