Preserving the privacy of sensitive relationships in graph data. PinKDD, 2007
Contents
Citation
author = {Zheleva, Elena and Getoor, Lise},
title = {Preserving the privacy of sensitive relationships in graph data},
booktitle = {Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD},
series = {PinKDD'07},
year = {2008},
isbn = {3-540-78477-2, 978-3-540-78477-7},
location = {San Jose, CA, USA},
pages = {153--171},
numpages = {19},
Online version
https://utd.edu/~mxk055100/courses/privacy08f_files/zheleva-pinkdd07-extended.pdf
Abstract from the paper
In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link reidentification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.
Summary
This paper addresses the problem of data sanitization to remove private information from datasets that are released to researchers and the public for data-mining. Most work in this area have focused on data that can be described by a single table with attribute information for each of the entries. Hoever real-world datasets often also exhibit dependencies between these entities. The authors study different anonymization techniques for such graph data. Specifically, unlike existing work that concentrate on hiding the identity of entities and/or their attributes (k-anonymity, l-diversity, t-closeness), the authors focused on the problem of inferring sensitive/private relationships from anonymized graph data.
