Title: Privacy preserving solution to prevent classification inference attacks in online social networks
Authors: Agrima Srivastava; G. Geethakumari
Addresses: Department of Computer Science and Information Systems, BITS Pilani, Hyderbad Campus, Hyderabad, 500038, India ' Department of Computer Science and Information Systems, BITS Pilani, Hyderbad Campus, Hyderabad, 500038, India
Abstract: In order to improve their business solutions the data holders often release the social network data and its structure to the third party. This data undergo node and attribute anonymisation before its release. This however does not prevent the users from inference attacks which an un-trusted third party or an adversary would carry out at their end by analysing the structure of the graph. Therefore, there is an utmost necessity to not only anonymise the nodes and their attributes but also to anonymise the edge sets in the released social network graph. Anonymising involves perturbing the actual data which results in utility loss. Ensuring utility and preserving privacy are inversely proportional to each other and is a challenging task. In this work we have proposed, implemented and verified an efficient utility based privacy preserving solution to prevent the third party inference attacks for an online social network graph.
Keywords: privacy; online social networks; privacy preserving data publishing; utility; network classification.
International Journal of Data Science, 2019 Vol.4 No.1, pp.31 - 44
Accepted: 10 Jul 2017
Published online: 11 Mar 2019 *