This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.

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Preventing Private Information Inference Attacks on Social
Networks
Abstract
Online social networks, such as Face book, are increasingly utilized by many
people. These networks allow users to publish details about themselves and to
connect to their friends. Some of the information revealed inside these networks is
meant to be private. Yet it is possible that corporations could use learning
algorithms on released data to predict undisclosed private information. In this
paper, we explore how to launch inference attacks using released social networking
data to predict undisclosed private information about individuals, such as their
political affiliation or sexual orientation. We then devise three possible sanitization
techniques that could be used in various situations. Then, we explore the
effectiveness of these techniques by implementing them on a dataset obtained from
a specific geographical region of the Face book social networking application and
attempting to use methods of collective inference to discover sensitive attributes of
the data set. We show that we can decrease the effectiveness of both local and
relational classification algorithms by using the sanitization methods we described.
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