research-article

A brief survey on anonymization techniques for privacy preserving publishing of social network data

Authors:

WoShun LukAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 10, Issue 2

Pages 12 - 22

https://doi.org/10.1145/1540276.1540279

Published: 20 December 2008 Publication History

Abstract

Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy preserving publishing of social network data becomes a more and more important concern. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches.

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 10, Issue 2

December 2008

98 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1540276

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Copyright © 2008 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 December 2008

Published in SIGKDD Volume 10, Issue 2

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Si MPei J(2024)Counterfactual Explanation of Shapley Value in Data CoalitionsProceedings of the VLDB Endowment10.14778/3681954.368200417:11(3332-3345)Online publication date: 1-Jul-2024
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