Generalization Algorithm For Prevent Inference Attacks In Social Network Data
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 3
Abstract
Online social networking has become one of the most popular activities on the web. Online social networks (OS Ns), such as Facebook, are increasingly utilized by many people. OS Ns allow users to control and customize what personal information is available to other users. These networks allow users to publish details about themselves and to connect to their friends. S ome of the information revealed inside these networks is meant to be private. A privacy breach occurs when sensitive information about the user, the information that an individual wants to keep from public, is disclosed to an adversary. Yet it is possible to use learning algorithms on released data to predict private information. Private information leakage could be an important issue in some cases. And explore how to launch inference attacks using released social networking data to predict private information. Desired use of data and individual privacy presents an opportunity for privacy-preserving social network data mining. Then devise three possible sanitization techniques that could be used in various situations. The effect of removing details and links in preventing sensitive information leakage. Removing details and friendship links together is the best way to reduce classifier accuracy. This is probably infeasible in maintaining the use of social networks. Explore the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. Decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods.
Authors and Affiliations
Chethana Nair, Neethu Krishna, Siby Abraham
Keywords
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