A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Proceedings of the 18th international conference on World wide web
On Link Privacy in Randomizing Social Networks
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Edge Anonymity in Social Network Graphs
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques
Small domain randomization: same privacy, more utility
Proceedings of the VLDB Endowment
Identity obfuscation in graphs through the information theoretic lens
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Efficiently anonymizing social networks with reachability preservation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Link disclosure between two individuals in a social network could be a privacy breach. To limit link disclosure, previous works modeled a social network as an undirected graph and randomized a link over the entire domain of links, which leads to considerable structural distortion to the graph. In this work, we address this issue in two steps. First, we model a social network as a directed graph and randomize the destination of a link while keeping the source of a link intact. The randomization ensures that, if the prior belief about the destination of a link is bounded by some threshold, the posterior belief, given the published graph, is no more than another threshold. Then, we further reduce structural distortion by a subgraph-wise perturbation in which the given graph is partitioned into several subgraphs and randomization of destination nodes is performed within each subgraph. The benefit of subgraph-wise perturbation is that it retains a destination node with a higher retention probability and replaces a destination node with a node from a local neighborhood. We study the trade-off of utility and privacy of subgraph-wise perturbation.