A preprocessing optimization applied to the cell suppression problem in statistical disclosure control

Serpell, Martin; Smith, James E.; Clark, Alistair; Staggemeier, Andrea T.

doi:10.1016/j.ins.2013.02.006

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…When tightening a table cells upper and lower bounds mathematical models act globally, they consider the whole table, whereas the shuttle algorithm acts locally only considering the row and column the cell resides in. This difference has been previously exploited in a pre-processing optimisation applied to the cell suppression problem (Serpell, 2013).…”

Section: The Algorithms To Be Comparedmentioning

confidence: 99%

Incorporating data quality improvement into supply–use table balancing

Serpell

2017

Economic Systems Research

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This paper investigates the benefits of using a boundary tightening algorithm to improve the quality of the data used in Supply and Use Table (SUTs) Balancing, building on similarities with certain approaches to Statistical Disclosure Control. Boundary tightening was shown to significantly improve the quality of the finally balanced SUTs well beyond that of existing techniques. Most notably, improvements occurred when boundary tightening was applied prior to the balancing processshowing that it can be used as a valuable preliminary to other approaches. It also multiplied the improvement in SUTs quality when more accurate updated information was added to the SUTs. The findings of this paper strongly suggest that this boundary tightening algorithm will improve the quality of the output of the balancing process and it is equally likely to be useful when applied to other processes that handle uncertain data.

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Section: The Algorithms To Be Comparedmentioning

confidence: 99%

Incorporating data quality improvement into supply–use table balancing

Serpell

2017

Economic Systems Research

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“…In most situations of generalization schemes, it mixes suppression operations in its process of data anonymization. It is without any doubts that there exists some theory or techniques to go on data anonymization only using suppression operation [37,38,39]. Like the category of generalization, there are five kinds of suppression, attribute suppression [34], record suppression [40], value suppression [41], cell suppression [42] and multidimensional suppression [43].…”

Section: Generalization and Suppressionmentioning

confidence: 99%

A Survey of Privacy Preserving Data Publishing using Generalization and Suppression

Yang¹,

Ma²,

Tang³

et al. 2014

Appl. Math. Inf. Sci.

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Nowadays, information sharing as an indispensable part appears in our vision, bringing about a mass of discussions about methods and techniques of privacy preserving data publishing which are regarded as strong guarantee to avoid information disclosure and protect individuals' privacy. Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios to satisfy privacy requirements, and keep data utility at the same time. K-anonymity has been proposed for privacy preserving data publishing, which can prevent linkage attacks by the means of anonymity operation, such as generalization and suppression. Numerous anonymity algorithms have been utilized for achieving k-anonymity. This paper provides an overview of the development of privacy preserving data publishing, which is restricted to the scope of anonymity algorithms using generalization and suppression. The privacy preserving models for attack is introduced at first. An overview of several anonymity operations follow behind. The most important part is the coverage of anonymity algorithms and information metric which is essential ingredient of algorithms. The conclusion and perspective are proposed finally.

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