The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e393" altimg="si20.svg"><mml:mi>k</mml:mi></mml:math>-anonymous microaggregation in big data

Rodríguez-Hoyos, Ana; Estrada-Jiménez, José; Rebollo-Monedero, David; Mezher, Ahmad Mohamad; Parra-Arnau, Javier; Forné, Jordi

doi:10.1016/j.engappai.2020.103531

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…The overall complexity of our method to match attributes and then generate clusters is O(n 2 +n). In comparison, the complexity of MDAV-based solution [38] to generate clusters is O(n 2 ), whereas the complexity to generate clusters in another scheme [6] is O(n 2 +n/k). This comparison states that the complexity to generate the clusters in our approach is not affected by the proposed methods, and it is the same as other MDAV-based solutions [6,38].…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…In comparison, the complexity of MDAV-based solution [38] to generate clusters is O(n 2 ), whereas the complexity to generate clusters in another scheme [6] is O(n 2 +n/k). This comparison states that the complexity to generate the clusters in our approach is not affected by the proposed methods, and it is the same as other MDAV-based solutions [6,38]. However, we have improved the information loss generated as a result of these methods that preserve the utility of the anonymized data (as shown in Figure 5).…”

Section: Evaluation Resultsmentioning

confidence: 99%

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Multivariate Microaggregation of Set-Valued Data

Daud

Shaheen

Ahmed

2022

ITC

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Data controllers manage immense data, and occasionally, it is released publically to help the researchers toconduct their studies. However, this publically shared data may hold personally identifiable information (PII)that can be collected to re-identify a person. Therefore, an effective anonymization mechanism is required toanonymize such data before it is released publically. Microaggregation is one of the Statistical Disclosure Control (SDC) methods that are widely used by many researchers. This method adapts the k-anonymity principle togenerate k-indistinguishable records in the same clusters to preserve the privacy of the individuals. However,in these methods, the size of the clusters is fixed (i.e., k records), and the clusters generated through these methods may hold non-homogeneous records. By considering these issues, we propose an adaptive size clusteringtechnique that aggregates homogeneous records in similar clusters, and the size of the clusters is determinedafter the semantic analysis of the records. To achieve this, we extend the MDAV microaggregation algorithm tosemantically analyze the unstructured records by relying on the taxonomic databases (i.e., WordNet), and thenaggregating them in homogeneous clusters. Furthermore, we propose a distance measure that determines theextent to which the records differ from each other, and based on this, homogeneous adaptive clusters are constructed. In experiments, we measured the cohesiveness of the clusters in order to gauge the homogeneity ofrecords. In addition, a method is proposed to measure information loss caused by the redaction method. In experiments, the results show that the proposed mechanism outperforms the existing state-of-the-art solutions.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Evaluation Resultsmentioning

confidence: 99%

Multivariate Microaggregation of Set-Valued Data

Daud

Shaheen

Ahmed

2022

ITC

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“…In order to ensure the reliability and credibility of the weights taken by each evaluation index and overcome the shortcomings of a single method for weight determination, this paper uses the distance function method to organically combine the two weights for comprehensive weight determination. The distance function method uses the concept of distance function to align the degree of difference between the subjective and objective weights with the degree of difference in the corresponding distribution coefficient [36][37][38], taking into account the subjective experience of the evaluator on the actual situation, and has important statistical significance. The calculation formula is:…”

Section: Determination Of Combination Weight Based On Distance Methodsmentioning

confidence: 99%

Research on Comprehensive Evaluation of Data Source Quality in Big Data Environment

Li¹,

Xu²,

Peng³

2021

IJCIS

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Data quality is the prerequisite of big data research and the basis of all data analysis, mining, and decision support. Therefore, a comprehensive fuzzy evaluation method for big data quality evaluation is proposed. Through the analysis of big data quality characteristics, a big data quality evaluation system for the whole process of data processing is constructed. The subjective weight and objective weight of each indicator are calculated through the analytic hierarchy process and entropy method. In order to overcome the subjective and one-sided shortcomings of the single weight determination method, the subjective weight and the objective weight are organically integrated through the distance function method to determine the combined weight of each indicator. The quantified result of big data quality is obtained through fuzzy calculation of membership degree. Finally the ranking results of the proposed method are compared with those of some existing multi-attribute decision-making (MADM) methods. The obtained results indicate that the proposed method is reasonable and efficient to deal with MADM problems. It can comprehensively measure the level of big data quality, and provide users with accurate and efficient quality evaluation results.

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“…The microaggregation method proposed by Rodríguez-Hoyos et al employed linear discriminant analysis to build microcells 22 . 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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The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for k-anonymous microaggregation in big data

Cited by 17 publications

References 39 publications

Multivariate Microaggregation of Set-Valued Data

Multivariate Microaggregation of Set-Valued Data

Research on Comprehensive Evaluation of Data Source Quality in Big Data Environment

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

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