Difference between revisions of "Preserving the privacy of sensitive relationships in graph data. PinKDD, 2007"
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| − | == Abstract from paper == | + | == 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. | 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 == | ||
| + | |||
| + | == Related papers == | ||
| + | |||
| + | == Study plan == | ||
Revision as of 22:54, 5 November 2012
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.
