20 20

Transactions on
Data Privacy
Foundations and Technologies

http://www.tdp.cat


Articles in Press

Accepted articles here

Latest Issues

Year 2024

Volume 17 Issue 1

Year 2023

Volume 16 Issue 3
Volume 16 Issue 2
Volume 16 Issue 1

Year 2022

Volume 15 Issue 3
Volume 15 Issue 2
Volume 15 Issue 1

Year 2021

Volume 14 Issue 3
Volume 14 Issue 2
Volume 14 Issue 1

Year 2020

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Year 2019

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Year 2018

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Year 2017

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Year 2016

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Year 2015

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Year 2014

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Year 2013

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Year 2012

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Year 2011

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Year 2010

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Year 2009

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Year 2008

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1


Volume 11 Issue 1


Alpha Anonymization in Social Networks using the Lossy-Join Approach

Kiran Baktha(a),(*), B K Tripathy(b)

Transactions on Data Privacy 11:1 (2018) 1 - 22

Abstract, PDF

(a) Department of Electronics and Communication Engineering, VIT University, Vellore, Tamil Nadu.

(b) Department of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu.

e-mail:sundarambaktha @hotmail.com; tripathybk @vit.ac.in


Abstract

Social networks contain important information about the society. Publishing them tends to have various advantages to data analyzers. However, privacy preservation in social networks has always been of primary concern. If these networks are published raw or with an ineffective anonymization technique, an adversary, even with limited background knowledge has the potential to extract the sensitive information present in the network. The Lossy-Join approach was used by Wong et al (2007) to develop an algorithm in order to achieve (α, k)-anonymity [14] in relational data. We extend this approach and develop an algorithm by using the same concept so that (α, k)-anonymity can be achieved in social networks. First, we run the proposed algorithm on a small network for illustration of its effect. Further, we test our approach on a real dataset from the United State Power grid and another large synthetic input in Erdös-Rényi graph generated by using R. Through experimental analysis, we establish that the efficiency of our algorithm is better than a general (α, k)-anonymity algorithm developed by Chakraborty et al [6] in 2016. We also propose a technique to add noisy sensitive labels into the model in case an anonymizer wishes a higher level of anonymization. The noise nodes required for anonymization are added so that they have minimal social importance.

* Corresponding author.

Follow us




Supports



ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; Umeå University; 90187 Umeå (Sweden); e-mail:tdp@tdp.cat
Note: TDP's web site does not use cookies. TDP does not keep information neither on IP addresses nor browsers. For the privacy policy access here.

 


Vicenç Torra, Last modified: 00 : 08 May 19 2020.