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 10 Issue 1


Enhancing the Utility of Anonymized Data by Improving the Quality of Generalization Hierarchies

Vanessa Ayala-Rivera(a),(*), Patrick McDonagh(b), Thomas Cerqueus(c), Liam Murphy(a), Christina Thorpe(a)

Transactions on Data Privacy 10:1 (2017) 27 - 59

Abstract, PDF

(a) Lero@UCD, School of Computer Science, University College Dublin, Ireland.

(b) CTO Reliability and Eco-Environmental Engineering Team, Alcatel-Lucent, Blanchardstown, D15, Ireland.

(c) R and D team, Lengow, Nantes, France.

e-mail:vanessa.ayala-rivera @ucdconnect.ie; patrick.mcdonagh @alcatel-lucent.com; thomas.cerqueus @lengow.com; liam.murphy @ucd.ie; christina.thorpe @ucd.ie


Abstract

The dissemination of textual personal information has become an important driver of innovation. However, due to the possible content of sensitive information, this data must be anonymized. A commonly-used technique to anonymize data is generalization. Nevertheless, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used as poorly-specified VGHs can decrease the usefulness of the resulting data. To tackle this problem, in our previous work we presented the Generalization Semantic Loss (GSL), a metric that captures the quality of categorical VGHs in terms of semantic consistency and taxonomic organization. We validated the accuracy of GSL using an intrinsic evaluation with respect to a gold standard ontology. In this paper, we extend our previous work by conducting an extrinsic evaluation of GSL with respect to the performance that VGHs have in anonymization (using data utility metrics). We show how GSL can be used to perform an a priori assessment of the VGHs' effectiveness for anonymization. In this manner, data publishers can quantitatively compare the quality of various VGHs and identify (before anonymization) those that better retain the semantics of the original data. Consequently, the utility of the anonymized datasets can be improved without sacrificing the privacy goal. Our results demonstrate the accuracy of GSL, as the quality of VGHs measured with GSL strongly correlates with the utility of the anonymized data. Results also show the benefits that an a priori VGH assessment strategy brings to the anonymization process in terms of time-savings and a reduction in the dependency on expert knowledge. Finally, GSL also proved to be lightweight in terms of computational resources.

* 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.