A Probabilistic Inference Attack on Suppressed Social Networks

Authors:
Baris Altop;Mehmet Ercan Nergiz;Yucel Saygin
Affiliations:
-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 5
Cited 0

Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

Proceedings of the 16th international conference on World Wide Web
Preservation of Privacy in Publishing Social Network Data

ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
Inferring private information using social network data

Proceedings of the 18th international conference on World wide web
Preserving Privacy in Social Networks Against Neighborhood Attacks

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Inferring privacy information from social networks

ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Social Networks (SNs) are widely used by internet users to share personal information, which also raises every privacy concern. Hence most service providers offer various preference-based privacy policies, allowing users to suppress any information under their accounts in case they do not wish to share it with public. In this paper, we show that such policies are not sufficient to provide privacy mainly because they do not allow users to control data belonging to other users they are linked with. We show experimentally that one can predict a suppressed boolean label (e.g, being rich or having voted for a specific political party) of a node from other released information in neighboring nodes when there is a known correlation between the links and the label.