LORA: link obfuscation by randomization in graphs

Authors:
Qian Xiao;Zhengkui Wang;Kian-Lee Tan
Affiliations:
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore;NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore;NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore and School of Computing, National University of Singapore, Singapore
Venue:
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Year:
2011

Citing 10
Cited 0

Spectral Characterizations of Some Distance-Regular Graphs

Journal of Algebraic Combinatorics: An International Journal
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

Proceedings of the 16th international conference on World Wide Web
Towards identity anonymization on graphs

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks

Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Differentially Private Graph Estimator

ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
k-automorphism: a general framework for privacy preserving network publication

Proceedings of the VLDB Endowment
k-symmetry model for identity anonymization in social networks

Proceedings of the 13th International Conference on Extending Database Technology
K-isomorphism: privacy preserving network publication against structural attacks

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Identity obfuscation in graphs through the information theoretic lens

ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a randomization scheme, LORA (Link Obfuscation by Randomization), to obfuscate edge existence in graphs. Specifically, we extract the source graph's hierarchical random graph model and reconstruct the released graph randomly with this model. We show that the released graph can preserve critical graph statistical properties even after a large number of edges have been replaced. To measure the effectiveness of our scheme, we introduce the notion of link entropy to quantify its privacy-preserving strength wrt the existence of edges.