20 20

Transactions on
Data Privacy
Foundations and Technologies


Articles in Press

Accepted articles here

Latest Issues

Year 2019

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 9 Issue 2

Lightning: Utility-Driven Anonymization of High-Dimensional Data

Fabian Prasser(a),(*), Raffael Bild(a), Johanna Eicher(a), Helmut Spengler(a), Florian Kohlmayer(a), Klaus A. Kuhn(a)

Transactions on Data Privacy 9:2 (2016) 161 - 185

Abstract, PDF

(a) Chair of Biomedical Informatics, Department of Medicine, Technical University of Munich (TUM), Germany.

e-mail:firstname.lastname @tum.de; firstname.lastname @tum.de; firstname.lastname @tum.de; firstname.lastname @tum.de; firstname.lastname @tum.de; firstname.lastname @tum.de


The ARX Data Anonymization Tool is a software for privacy-preserving microdata publishing. It implements methods of statistical disclosure control and supports a wide variety of privacy models, which are used to specify disclosure risk thresholds. Data is mainly transformed with a combination of two methods: (1) global recoding with full-domain generalization of attribute values followed by (2) local recoding with record suppression. Within this transformation model, given a dataset with low dimensionality, it is feasible to compute an optimal solution with minimal loss of data quality. However, combinatorial complexity renders this approach impracticable for high-dimensional data. In this article, we describe the Lightning algorithm, a simple, yet effective, utility-driven heuristic search strategy which we have implemented in ARX for anonymizing high-dimensional datasets. Our work improves upon existing methods because it is not tailored towards specific models for measuring disclosure risks and data utility. We have performed an extensive experimental evaluation in which we have compared our approach to state-of-the-art heuristic algorithms and a globally-optimal search algorithm. In this process, we have used several real-world datasets, different models for measuring data utility and a wide variety of privacy models. The results show that our method outperforms previous approaches in terms output quality, even when using k-anonymity, which is the model for which previous work has been designed.

* Corresponding author.

Follow us


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; U. of Skövde; PO Box 408; 54128 Skövde; (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: 01 : 14 August 28 2016.