The ARGUS Software in the CASC-Project

Hundepool, Anco

doi:10.1007/978-3-540-25955-8_26

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…However, most of them have been included in the τ -Argus software (Hundepool et al, 2008), freely available from http://neon.vb.cbs.nl/casc/tau.htm, and being used by several European NSAs. In particular, this package includes the Benders decomposition of Fischetti and Salazar-González (2001), the shortest paths heuristic of Castro (2007a), and the two (infeasible) heuristics of deWolf (2002) and Giessing and Repsilber (2002).…”

Section: Computational Evaluation Of Approaches For Cspmentioning

confidence: 99%

Recent advances in optimization techniques for statistical tabular data protection

Castro

2012

European Journal of Operational Research

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One of the main services of National Statistical Agencies (NSAs) for the current Information Society is the dissemination of large amounts of tabular data, which is obtained from microdata by crossing one or more categorical variables. NSAs must guarantee that no confidential individual information can be obtained from the released tabular data. Several statistical disclosure control methods are available for this purpose. These methods result in large linear, mixed integer linear, or quadratic mixed integer linear optimization problems. This paper reviews some of the existing approaches, with an emphasis on two of them: cell suppression problem (CSP) and controlled tabular adjustment (CTA). CSP and CTA have concentrated most of the recent research in the tabular data protection field. The particular focus of this work is on methods and results of practical interest for end-users (mostly, NSAs). Therefore, in addition to the resulting optimization models and solution approaches, computational results comparing the main optimization techniques-both optimal and heuristic-using real-world instances are also presented.

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Section: Computational Evaluation Of Approaches For Cspmentioning

confidence: 99%

Recent advances in optimization techniques for statistical tabular data protection

Castro

2012

European Journal of Operational Research

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“…This number is computed using the function measure_risk of package sdcMicro in R (see [41]). This measure of individuals in risk is computed as follows (see [19]):…”

Section: Resultsmentioning

confidence: 99%

On the use of fuzzy partitions to protect data

Quirós

Alonso

Dı́az

et al. 2014

ICA

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Data protection is one of the most challenging tasks nowadays due to the huge amount of information that can be shared and crossed from different sources. Releasing microdata is a way to protect data, mainly in the economic and medical field. However, this kind of data can experience privacy attacks. This paper proposes the use of fuzzy sets as a way to improve the protection of privacy in microdata. Then, traditional definitions of k-anonymity, l-diversity and t-closeness are extended. The performance of these new approaches is checked in terms of the risk index.

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“…The heuristic of Section 4-including the lowerbounding procedure of Section 5-has been implemented in C, It is currently included in the r-Argus package (Hundepool 2004) for tabular data protection, which is used by several European NSAs. For testing purposes, we considered two classes of problems.…”

Section: Computational Resultsmentioning

confidence: 99%

“…As shown by the computational results, it is much faster than heuristics based on minimum-cost network problems for large tables. The new approach has been included in the T-Argus package (Hundepool 2004) in the scope of the project IST-2000-25069 CASC (Computational Aspects of Statistical Confidentiality), funded by the European Union. That project involved 14 institutions from five European countries, including the NSAs of Catalonia, Germany, Italy, The Netherlands, Spain, and The United Kingdom.…”

Section: Introductionmentioning

confidence: 99%

A Shortest-Paths Heuristic for Statistical Data Protection in Positive Tables

Castro

2007

INFORMS Journal on Computing

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i ' N ational statistical agencies (NSAs) routinely release large amounts of tabular information. Prior to dissemination, tabular data need to be processed to avoid disclosure of individual corifidential information. Cell suppression is one of the most widely used techniques by NSAs. Optimal procedures for cell suppression are computationally expensive with large real-world data sets, so heuristic procedures are used. Most heuristics for positive tables (i.e., cell values are normegative) rely on the solution of minimum-cost network-flows subproblems. A very efficient heuristic based on shortest paths already exists, but it is only appropriate for general tables (i.e., cell values can be either positive or negative), whereas in practice most tables are positive. We present a method that sensibly combines and improves previous approaches, overcoming some of their drawbacks: it is designed for positive tables and requires only the solution of shortest-path subproblems-therefore being much more efficient than other network-flows heuristics. We report extensive computational experience in the solution of randomly generated and real-world instances, comparing the heuristic with alternative procedures. The results show that the method, currently included in a software package for statistical data protection, fits NSA needs: it is extremely efficient and provides good solutions.

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The ARGUS Software in the CASC-Project

Cited by 10 publications

References 3 publications

Recent advances in optimization techniques for statistical tabular data protection

Recent advances in optimization techniques for statistical tabular data protection

On the use of fuzzy partitions to protect data

A Shortest-Paths Heuristic for Statistical Data Protection in Positive Tables

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