Comparison of microaggregation approaches on anonymized data quality

Lin, Jun-Lin; Chang, Pei‐Chann; Liu, Julie Yu‐Chih; Wen, Tsung-Hsien

doi:10.1016/j.eswa.2010.05.071

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…This method applies an extension of improved MHM (Mortazavi et al 2013) to the sequence of data records in a TSP tour. For a more comprehensive survey about microaggregation methods, interested readers are recommended to refer to (Fayyoumi and Oommen 2010;Lin et al 2010).…”

Section: Related Workmentioning

confidence: 99%

“…For example, Herranz et al showed that the classification accuracies of different classifiers trained on protected datasets have a meaningful correlation to the SDC utility measures of these datasets. Lin et al utilized the classification accuracy as a measure to quantify microaggregation techniques (Lin et al 2010). Similarly, different regression models can be constructed after microaggregation (López 2011;Schmid et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

Mortazavi

Jalili

2015

Data Min Knowl Disc

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Microaggregation is a masking mechanism to protect confidential data in a public release. This technique can produce a k-anonymous dataset where data records are partitioned into groups of at least k members. In each group, a representative centroid is computed by aggregating the group members and is published instead of the original records. In a conventional microaggregation algorithm, the centroids are computed based on simple arithmetic mean of group members. This naïve formulation does not consider the proximity of the published values to the original ones, so an intruder may be able to guess the original values. This paper proposes a disclosureaware aggregation model, where published values are computed in a given distance from the original ones to attain a more protected and useful published dataset. Empirical results show the superiority of the proposed method in achieving a better trade-off point between disclosure risk and information loss in comparison with other similar anonymization techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

Mortazavi

Jalili

2015

Data Min Knowl Disc

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“…Microaggregation was also tested in terms of predictive performance. A comparative study of several microaggregation approaches w.r.t predictive analysis and 𝑘-anonymity measure shows that the prediction accuracy of a classifier based on a de-identified data set is not always worse than baseline [99]. For instance, some results show higher accuracy when compared to the baseline due to the reduction of variance in the de-identified data set.…”

Section: Impact On Predictive Performancementioning

confidence: 99%

Survey on Privacy-Preserving Techniques for Data Publishing

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

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The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques. However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individuals' privacy while maintaining the interpretability of the data, i.e. its usefulness. Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing privacy-preserving techniques used in microdata de-identification, privacy measures suitable for several disclosure types and, information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review taxonomies of privacy-preserving techniques, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.

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“…These changes may introduce a number of different tuning parameters that must be set by the data publisher in the new setting, which makes complex the process of anonymization. More thorough surveys about microaggregation methods, are presented in [13,14].…”

Section: Related Workmentioning

confidence: 99%

“…The SimpleHeuristic function initializes an empty pool of a predefined size of (line 5). If such cycles of length 2 with negative weights are found, they are added to the pool (lines [6][7][8][9][10][11][12][13][14]. If no such cycles are found, the assignment is returned without any change (line 15).…”

Section: B the Proposed Algorithmmentioning

confidence: 99%

Iterative constraint satisfaction method for microaggregation problem

Mortazavi

Jalili

2014

2014 11th International ISC Conference on Information Security and Cryptology

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In this paper, we propose a novel microaggregation algorithm to produce useful data in privacy preserving data publishing. Microaggregation is a clustering problem with known minimum and maximum group size constraints. We propose a local search algorithm that iteratively satisfies necessary constraints of an optimal solution of the problem. The algorithm solves the problem in operations. Experimental results on real and synthetic data sets with different distributions confirm the effectiveness of the method.

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Comparison of microaggregation approaches on anonymized data quality

Cited by 9 publications

References 17 publications

Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

Survey on Privacy-Preserving Techniques for Data Publishing

Iterative constraint satisfaction method for microaggregation problem

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