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Algorithms
Special Issues
Statistical Disclosure Control for Microdata
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Statistical Disclosure Control for Microdata

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 8999

Special Issue Editor

Prof. Dr. Matthias Templ

E-Mail Website
Guest Editor
School of Business, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland
Interests: computational statistics; official statistics; compositional data analysis; robust statistics; statistical modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of statistical disclosure control and anonymization of data to this Special Issue, “Statistical Disclosure Control”. Privacy, confidentiality, and anonymization of data have recently received a great deal of attention, caused by the increased sensibility of this topic in politics and society and related new laws in privacy. Research on this topic is in special focus.

We are looking for new and innovative approaches for the anonymization of data with personal information. High-quality papers are solicited to address both theoretical and practical issues of methods in statistical disclosure control. Potential topics include, but are not limited to, re-identification risk measurement, anonymization of data, utility of anonymized data, and methods to create synthetic data. Complex case studies with complex data from health, official statistics, or any other kind of data including personal information are also welcome.

Dr. Matthias Templ
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • confidentiality
  • data anonymization
  • risk of re-identification
  • utility of anonymized data
  • tabular data anonymization
  • synthetic data

Published Papers (2 papers)

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Research

20 pages, 877 KiB  
Article
Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
by Bernhard Meindl and Matthias Templ
Algorithms 2019, 12(9), 191; https://0-doi-org.brum.beds.ac.uk/10.3390/a12090191 - 10 Sep 2019
Cited by 2 | Viewed by 4617
Abstract
The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how [...] Read more.
The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app. Full article
(This article belongs to the Special Issue Statistical Disclosure Control for Microdata)
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17 pages, 662 KiB  
Article
Algorithmic Matching Attacks on Optimally Suppressed Tabular Data
by Kazuhiro Minami and Yutaka Abe
Algorithms 2019, 12(8), 165; https://0-doi-org.brum.beds.ac.uk/10.3390/a12080165 - 11 Aug 2019
Cited by 2 | Viewed by 3716
Abstract
The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based [...] Read more.
The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, we find that every deterministic CSP algorithm is vulnerable to an adversary who possesses the knowledge of that algorithm. We devise a matching attack scheme that narrows down the ranges of sensitive cell values by matching the suppression pattern of an original table with that of each candidate table. Our experiments show that actual ranges of sensitive cell values are significantly narrower than those assumed by the previous CSP algorithms. Full article
(This article belongs to the Special Issue Statistical Disclosure Control for Microdata)
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