ARGUS, Software Packages for Statistical Disclosure Control

Hundepool, Anco; Willenborg, Leon

doi:10.1007/978-3-662-01131-7_45

Cited by 47 publications

(62 citation statements)

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“…As these tables have to be protected against the risk of disclosure, the software package t-ARGUS [1] can be applied. Two common strategies to protect against the risk of disclosure are table redesign and the suppression of individual values.…”

Section: The Release Of Tables With T-argusmentioning

confidence: 99%

“…For many surveys microdata for researchers are released. The software package m-ARGUS [2] is of help in producing these microdata for researchers. For the microdata for researchers Statistics Netherlands uses the following set of rules:…”

Section: The Release Of Tables With T-argusmentioning

confidence: 99%

“…The tables produced by Statistics Netherlands on the basis of the microdata of surveys have to be protected against the risk of disclosure. Therefore the software package t-ARGUS [1] can be applied on the tables produced. More information about t-ARGUS and how this package can be applied are given in section 2.…”

Section: Introductionmentioning

confidence: 99%

“…More information about t-ARGUS and how this package can be applied are given in section 2. In section 3 it is explained how microdata for research and public use microdata files can be produced using the software package m-ARGUS [2]. The option for bona fide researchers to work on-site on richer microdata files is explained in section 4.…”

Section: Introductionmentioning

confidence: 99%

“…Public use microdata files contain much less detailed information than microdata for research. The software package m-ARGUS [2] is also of help in producing public use microdata files. For the public use microdata files Statistics Netherlands uses the following set of rules:…”

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confidence: 99%

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Statistical Disclosure Control

Bethlehem¹

2009

Applied Survey Methods

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Section: The Release Of Tables With T-argusmentioning

confidence: 99%

Section: The Release Of Tables With T-argusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Statistical Disclosure Control

Bethlehem¹

2009

Applied Survey Methods

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Flexible data anonymization using ARX—Current status and challenges ahead

et al. 2020

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The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk-reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality-while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.

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Integrating Private Databases for Data Analysis

Wang

Fung

Dong

2005

Intelligence and Security Informatics

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Abstract. In today's globally networked society, there is a dual demand on both information sharing and information protection. A typical scenario is that two parties wish to integrate their private databases to achieve a common goal beneficial to both, provided that their privacy requirements are satisfied. In this paper, we consider the goal of building a classifier over the integrated data while satisfying the k-anonymity privacy requirement. The k-anonymity requirement states that domain values are generalized so that each value of some specified attributes identifies at least k records. The generalization process must not leak more specific information other than the final integrated data. We present a practical and efficient solution to this problem.

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ARGUS, Software Packages for Statistical Disclosure Control

Cited by 47 publications

References 1 publication

Statistical Disclosure Control

Statistical Disclosure Control

Flexible data anonymization using ARX—Current status and challenges ahead

Integrating Private Databases for Data Analysis

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