Proceedings ArticleDOI
Privacy Preservation in Social networks through alpha: anonymization techniques
Saptarshi Chakraborty,B. K. Tripathy +1 more
- pp 1602-1603
TLDR
An (a, k) anonymity model based on the eigenvector centrality value of the nodes present in the raw graph is proposed and further extend it to propose ( a, l) diversity model and recursive (a), c, l diversity model which can handle the protection of the sensitive attributes associated with a particular actor.Abstract:
We propose an (a, k) anonymity model based on the eigenvector centrality value of the nodes present in the raw graph and further extend it to propose (a, l) diversity model and recursive (a, c, l) diversity model which can handle the protection of the sensitive attributes associated with a particular actor. For anonymization purpose, we applied noise node addition technique to generate the anonymized graphs so that the structural property of the raw graph is preserved. Our proposed methods add noise nodes with very minimal social importance. We applied eigenvector centrality concept over traditional degree centrality concept to prevent mixing of highly influential nodes with less influential nodes in the equivalence groups.read more
Citations
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Journal ArticleDOI
Alpha-anonymization techniques for privacy preservation in social networks
TL;DR: An (α, k) anonymity model based on the eigenvector centrality of the nodes present in the raw graph is proposed, which can also protect the sensitive attribute values associated with a particular actor.
Alpha Anonymization in Social Networks using the Lossy-Join Approach.
Kiran Baktha,B. K. Tripathy +1 more
TL;DR: The efficiency of this algorithm is better than a general (α, k) anonymity algorithm developed by Chakraborty et al [6] in 2016 and a technique to add noisy sensitive labels into the model in case an anonymizer wishes a higher level of anonymization is proposed.
Journal ArticleDOI
SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels
TL;DR: A framework along with an algorithm for extracting the immune graph and supplementary trees and the practical evaluation of the cancer code label of individuals indicates the effectiveness of the SNI (social network immunization) method.
References
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Proceedings ArticleDOI
Towards identity anonymization on graphs
Kun Liu,Evimaria Terzi +1 more
TL;DR: This work formally defines the graph-anonymization problem that, given a graph G, asks for the k-degree anonymous graph that stems from G with the minimum number of graph-modification operations, and devise simple and efficient algorithms for solving this problem.
Proceedings ArticleDOI
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
TL;DR: It is proved that the optimal (α, k)-anonymity problem is NP-hard, and a local-recoding algorithm is proposed which is more scalable and result in less data distortion.
Journal ArticleDOI
Resisting structural re-identification in anonymized social networks
TL;DR: In this paper, the authors quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary, and propose a novel approach to anonymizing network data that models aggregate network structure and then allows samples to be drawn from that model.
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k-automorphism: a general framework for privacy preserving network publication
Lei Zou,Lei Chen,M. Tamer Özsu +2 more
TL;DR: This paper proposes k-automorphism to protect against multiple structural attacks and develops an algorithm (called KM) that ensures k-Automorphism and discusses an extension of KM to handle "dynamic" releases of the data.
Journal ArticleDOI
Protecting Sensitive Labels in Social Network Data Anonymization
TL;DR: A k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals is defined and a novel anonymization methodology based on adding noise nodes is proposed.