Hybrid microaggregation for privacy preserving data mining

Abidi, Balkis; Yahia, Sadok Ben; Perera, Charith

doi:10.1007/s12652-018-1122-7

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Shi et al suggested to use distance metrics and information entropy to aggregate data into equivalent groups, thereby, ensuring the protection of individual privacy while minimizing information loss 19 . B. Abidi et al introduced a microaggregation method based on fuzzy possibilistic clustering 21 which proposes to study the distribution of confidential attributes within each sub-dataset and the privacy parameter K is determined by preserving the diversity of confidential attributes within the anonymized microdata. The microaggregation method proposed by Rodríguez-Hoyos et al employed linear discriminant analysis to build microcells 22 .…”

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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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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show abstract

“…Distance metrics and information entropy were used to aggregate data into equivalence groups, which ensure the protection of individual privacy while minimizing information loss. Abidi et al [25] introduced a new microaggregation method based on fuzzy possibilistic clustering, which proposes to study the distribution of confidential attributes within each sub-dataset and the privacy parameter K is determined by preserving the diversity of confidential attributes within the anonymized microdata. Ana et al [26] proposed a k-anonymous microaggregation method via linear discriminant analysis.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Dynamic Data Release Against Synonymous Linkage Based on Micro Aggregation

Yan¹,

Herman²,

Mahmood³

et al. 2021

Preprint

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The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensifified 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 academic 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 micro aggregation. 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 micro-aggregation anonymous method. The dynamic update program is introduced into the proposed micro-aggregation 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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“…Micro-aggregation can be applied to both continuous and categorical data without the need for the data author to create generalised categories. Various approaches to perform micro-aggregation were proposed; for example, a hybrid micro-aggregation approach which is "based on fuzzy possibilistic clustering" [24].…”

Section: Generic Privacy-preserving Data Transformation Approachesmentioning

confidence: 99%

Privacy-Preserving Process Mining in Healthcare

Pika

Wynn

Budiono

et al. 2020

IJERPH

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Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

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Hybrid microaggregation for privacy preserving data mining

Cited by 10 publications

References 42 publications

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

Privacy Preserving Dynamic Data Release Against Synonymous Linkage Based on Micro Aggregation

Privacy-Preserving Process Mining in Healthcare

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