The ARGUS Software in CENEX

Hundepool, Anco

doi:10.1007/11930242_28

Cited by 3 publications

(4 citation statements)

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“…This code has been included in the τ -Argus package since version 4.1.0 [13,19] (which can be freely obtained from http://neon.vb.cbs.nl/casc/tau.htm), used by European (and some non-European) statistical agencies. The code may use up to five different solvers, both commercial and open source.…”

Section: Computational Resultsmentioning

confidence: 99%

“…This multiobjective approach has been recently implemented within the FP7-INFRA-2010-262608 project funded by the European Union, with the participation, among others, of the NSAs of Germany, Netherlands, Finland, Sweden and Slovenia. The multiobjective LO-CTA software has been included in the τ -Argus package since version 4.1.0 [13,19] (http://neon.vb.cbs.nl/casc/tau.htm), used by many European national statistical institutes for the protection of tabular data.…”

Section: Introductionmentioning

confidence: 99%

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A linear optimization-based method for data privacy in statistical tabular data

Castro

González

2017

Optimization Methods and Software

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National Statistical Agencies routinely disseminate large amounts of data. Prior to dissemination these data have to be protected to avoid releasing confidential information. Controlled tabular adjustment (CTA) is one of the available methods for this purpose. CTA formulates an optimization problem that looks for the safe table which is closest to the original one. The standard CTA approach results in a mixed integer linear optimization (MILO) problem, which is very challenging for current technology. In this work we present a much less costly variant of CTA that formulates a multiobjective linear optimization (LO) problem, where binary variables are pre-fixed, and the resulting continuous problem is solved by lexicographic optimization. Extensive computational results are reported using both commercial (CPLEX and XPRESS) and open source (Clp) solvers, with either simplex or interior-point methods, on a set of real instances. Most instances were successfully solved with the LO-CTA variant in less than one hour, while many of them are computationally very expensive with the MILO-CTA formulation. The interior-point method outperformed simplex in this particular application.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A linear optimization-based method for data privacy in statistical tabular data

Castro

González

2017

Optimization Methods and Software

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“…In subsampling, we do not publish every record, but rather sample records with a (possibly nonuniform) sampling rate. Many of these methods have been implemented in software packages 41,42 . In addition to changing the data that is published, in practice, statistical offices limit disclosure further by keeping the parameters of the algorithms, such as the actual swap rate or the sampling rate secret.…”

Section: The Pastmentioning

confidence: 99%

Quo vadis, data privacy?

Gehrke

2012

Annals of the New York Academy of Sciences

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Society can gain much value from Big Data. We can study census data to learn where to allocate public resources, medical records from hospitals to fight diseases, or data about students and teachers to evaluate the effectiveness of various approaches to learning and teaching. In all of these scenarios, we need to limit statistical disclosure: we want to release accurate statistics about the data while preserving the privacy of the individuals who contributed it. This paper gives an overview of recent advances and open challenges in the field, focusing on methods that probably limit how much an adversary can learn from a data release.

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“…This package has been largely improved within the FP7-INFRA-2010-262608 project funded by the European Union, with the participation, among others, of the national statistical institutes of Germany, Netherlands, Finland, Sweden and Slovenia. This CTA software is included in the tauArgus package [25] (http://neon.vb.cbs.nl/casc/tau.htm), used for many European national statistical institutes for the protection of tabular data. Among the recent literature on CTA variants we find [8,24].…”

Section: Introductionmentioning

confidence: 99%

Fix-and-relax approaches for controlled tabular adjustment

Baena

Castro

González

2015

Computers & Operations Research

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Controlled tabular adjustment (CTA) is a relatively new protection technique for tabular data protection. CTA formulates a mixed integer linear programming problem, which is challenging for tables of moderate size. Even finding a feasible initial solution may be a challenging task for large instances. On the other hand, end users of tabular data protection techniques give priority to fast executions and are thus satisfied in practice with suboptimal solutions. This work has two goals. First, the fix-and-relax (FR) strategy is applied to obtain good feasible initial solutions to large CTA instances. FR is based on partitioning the set of binary variables into clusters to selectively explore a smaller branch-and-cut tree. Secondly, the FR solution is used as a warm start for a block coordinate descent (BCD) heuristic (approach named FR+BCD); BCD was confirmed to be a good option for large CTA instances in an earlier paper by the second and third co-authors (Computers & Operations Research 2011). We report extensive computational results on a set of real-world and synthetic CTA instances. FR is shown to be competitive compared to CPLEX branch-and-cut in terms of quickly finding either a feasible solution or a good upper bound. FR+BCD improved the quality of FR solutions for approximately 25% and 50% of the synthetic and real-world instances, respectively. FR or FR+BCD provided similar or better solutions in less CPU time than CPLEX for 73% of the difficult real-world instances.

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The ARGUS Software in CENEX

Cited by 3 publications

References 1 publication

A linear optimization-based method for data privacy in statistical tabular data

A linear optimization-based method for data privacy in statistical tabular data

Quo vadis, data privacy?

Fix-and-relax approaches for controlled tabular adjustment

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