Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets

Pallarès, Esteve; Rebollo-Monedero, David; Rodríguez-Hoyos, Ana; Estrada-Jiménez, José; Mezher, Ahmad Mohamad; Forné, Jordi

doi:10.1016/j.eswa.2019.113086

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Due to such demanding requirements, privacy related data are overshadowed. Thus, the perspective of privacy, we feel that any improvement in preserving data utility without a price in (computing) is not negligible and some are currently being purposed in this direction [43,35].…”

Section: Discussion On Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Preserving empirical data utility ink-anonymous microaggregation via linear discriminant analysis

Rodríguez-Hoyos¹,

Rebollo-Monedero²,

Estrada-Jiménez³

et al. 2020

Engineering Applications of Artificial Intelligence

Self Cite

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Section: Discussion On Resultsmentioning

confidence: 99%

“…If protection mechanisms cannot cope with the (sometimes real-time) requirements of modern applications, they render unusable no matter how much utility is preserved. A few works have been proposed recently in this direction [35,42].…”

Section: Introductionmentioning

confidence: 99%

Preserving empirical data utility ink-anonymous microaggregation via linear discriminant analysis

Rodríguez-Hoyos¹,

Rebollo-Monedero²,

Estrada-Jiménez³

et al. 2020

Engineering Applications of Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

“…They also proposed several strategies to simplify the distance calculations and element sorting operations for data microaggregation 23 . Pallarès et al proposed an optimized prepartitioning strategy to reduce the running time of K -anonymous microaggregation on large datasets 24 .…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

Yan

Eyeleko

Mahmood

et al. 2022

Sci Rep

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The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

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“…Recent works have shown to follow similar approaches, harnessing known mechanisms from different domains (e.g., machine learning), aiming to increase the efficiency of privacy protection algorithms, not only in terms of runtime [12,13,14,15], but also in terms of resulting data utility. For instance, [16] developed an efficient clustering mechanism to deal with large databases while preserving the data utility through a partitioning method of a modified minimum spanning tree.…”

Section: Related Work and Impact Of This Approachmentioning

confidence: 99%

“…The evaluation of the computational performance of our methods has been conducted with three standardized data sets. These real data sets include "Large Census", "Quant Forest" and "USA House", which were previously used in [49,12]. The "Large Census" data set has 149,642 records and includes 13 numerical attributes; "Quant Forest" has 581,012 records, from which we use a random sample of 150,000 records, and 10 numerical attributes.…”

Section: Evaluation Criteria and Data Setsmentioning

confidence: 99%

The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for k-anonymous microaggregation in big data

Rodríguez-Hoyos

Estrada-Jiménez

Rebollo-Monedero

et al. 2020

Engineering Applications of Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets

Cited by 8 publications

References 47 publications

Preserving empirical data utility ink-anonymous microaggregation via linear discriminant analysis

Preserving empirical data utility ink-anonymous microaggregation via linear discriminant analysis

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for k-anonymous microaggregation in big data

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