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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Individual privacy in social influence networks

Authors: Sara Hajian, Tamir Tassa, Francesco Bonchi

Published in: Social Network Analysis and Mining | Issue 1/2016

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Abstract

Online social networking platforms have the possibility to collect an incredibly rich set of information about their users: the people they talk to, the people they follow and trust, the people they can influence, as well as their hobbies, interests, and topics in which they are authoritative. Analyzing these data creates fascinating opportunities for expanding our understanding about social structures and phenomena such as social influence, trust and their dynamics. At the same time, mining this type of rich information allows building novel online services, and it represents a great resource for advertisers and for building viral marketing campaigns. Sharing social-network graphs, however, raises important privacy concerns. To alleviate this problem, several anonymization methods have been proposed that aim at reducing the risk of a privacy breach on the published data while still allowing to analyze them and draw relevant conclusions. The bulk of those proposals only considers publishing the network structure, that is a simple (often undirected) graph. In this paper we study the problem of preserving users’ individual privacy when publishing information-rich social networks. In particular, we consider the obfuscation of users’ identities in a topic-dependent social influence network, i.e., a directed graph where each edge is enriched by a topic model that represents the strength of the social influence along the edge per topic. This information-rich graph is obviously much harder to anonymize than standard graphs. We propose here to obfuscate the identity of nodes in the network by randomly perturbing the network structure and the topic model. We then formalize our privacy notion, k-obfuscation, and show how to evaluate the level of obfuscation under a strong adversarial assumption. Experiments on two social networks confirm that randomization can successfully protect the privacy of the users while maintaining high-quality data for applications, such as influence maximization for viral marketing.
next article A review of features for the discrimination of twitter users: application to the prediction of offline influence

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Footnotes
1
For instance, in Twitter in-degree and out-degree are public information.
 
2
The datasets we use for experiments, as well as our random obfuscation software and the software for quantifying the level of obfuscation achieved, are available at http://​bit.​ly/​1pt9NJp.
 
3
It is worth noting that, although called “set”, a seed set as output by a TIM query is an ordered list of nodes.
 
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Metadata
Title
Individual privacy in social influence networks
Authors
Sara Hajian
Tamir Tassa
Francesco Bonchi
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-015-0312-y

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