Privacy preserving solution to prevent classification inference attacks in online social networks Online publication date: Mon, 18-Mar-2019
by Agrima Srivastava; G. Geethakumari
International Journal of Data Science (IJDS), Vol. 4, No. 1, 2019
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook