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Algorithmic Matching Attacks on Optimally Suppressed Tabular Data

1,2,3,* and 1,3
1
Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies (SOKENDAI), Shonan Village, Hayama, Kanagawa 240-0193, Japan
2
The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
3
National Statistics Center, 19-1 Wakamatsu-cho, Shinjuku-Ku, Tokyo 162-8668, Japan
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(8), 165; https://doi.org/10.3390/a12080165
Received: 30 June 2019 / Revised: 6 August 2019 / Accepted: 8 August 2019 / Published: 11 August 2019
(This article belongs to the Special Issue Statistical Disclosure Control for Microdata)
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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 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. View Full-Text
Keywords: statistical disclosure control; cell suppression problem; integer linear programming statistical disclosure control; cell suppression problem; integer linear programming
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Minami, K.; Abe, Y. Algorithmic Matching Attacks on Optimally Suppressed Tabular Data. Algorithms 2019, 12, 165.

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