A Survey of Privacy-Preservation of Graphs and Social Networks

Wu, Xintao; Ying, Xiaowei; Liu, Kun; Chen, Lei

doi:10.1007/978-1-4419-6045-0_14

Cited by 115 publications

(73 citation statements)

References 47 publications

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“…Wu et al [39] Classified the existing anonymization techniques on simple graphs in three main categories: K-anonymity-based privacy preservation through edge modification, probabilistic privacy preservation through edge randomization, and privacy reservation through generalization 2011…”

Section: Resultsmentioning

confidence: 99%

A Survey on Privacy Preserving Techniques for Social Network Data

Maheswari

2017

Asian J Pharm Clin Res

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In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone’s life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone’s personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can’t apply directly to the structured complex graph data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.

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Section: Resultsmentioning

confidence: 99%

A Survey on Privacy Preserving Techniques for Social Network Data

Maheswari

2017

Asian J Pharm Clin Res

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“…Social network is modeled as the graph [8] with vertices, edges and relationship between the vertices. According to dynamic network, relationship between the individuals changes when time changes literally.…”

Section: Privacy Preservation Modelsmentioning

confidence: 99%

Identity Disclosure Protection in Dynamic Networks Using KW – Structural Diversity Anonymity

Gowthamy¹,

Uma²

2016

IJITE

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“…2010 Wu et al [51] Categorized the existing anonymization methods on simple graphs in 3 main categories: Kanonymity based privacy preservation via edge modification, probabilistic privacy preservation via edge randomization, privacy reservation via generalization. 2011…”

Section: Validated On Facebook For 700 Users 2010mentioning

confidence: 99%