Multi-PFKCN : A fuzzy possibilistic clustering algorithm based on neural network

Abidi, Balkis; Yahia, Sadok Ben

doi:10.1109/fuzz-ieee.2013.6622419

Cited by 2 publications

(4 citation statements)

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“…Therewith, we are of the view that to prevent against identity disclosure, the parameter k should be fixed according to the distribution of sensitive information, in order to ensure that the k-anonymous records fulfil a diversity of sensitive information and so prevent attribute disclosure. To meet these issues, we propose a new method, called HM-PFSOM, for hybrid microaggregation, by using PFSOM algorithm for fuzzy possibilistic clustering (Abidi and Ben Yahia 2013). The main idea of the HM-PFSOM algorithm consists in splitting the original dataset into disjoint sub-datasets, in such a way that data within the same sub-dataset must be similar to some extend.…”

Section: Discussion and Motivationmentioning

confidence: 99%

“…The HM-PFSOM algorithm starts by splitting the original microdata into disjoint sub-microdata (line 2), in such a way that data sharing similar characteristic of quasi-identifiers are gathered in the same sub-microdata. To do so, the HM-PFSOM algorithm, relies on fuzzy possibilistic clustering process (Abidi and Ben Yahia 2013). Thereafter, the partitioning process of the microaggregation can be applied independently on each sub-microdata (lines 4 − 17).…”

Section: The Hm-pfsom Algorithm For Hybrid Microaggregationmentioning

confidence: 99%

“…In this respect, we have proposed in a previous work a fuzzy possibilistic clustering algorithm, called Possibilistic Fuzzy Self Organising Map (PFSOM) (Abidi and Ben Yahia 2013), able to split a given dataset into c disjoint clusters, via a multi-level process. Where c corresponds to the optimal number of clusters contained in a dataset (Abidi et al 2012).…”

Section: Fuzzy Possibilistic Clustering For Hybrid Microaggregationmentioning

confidence: 99%

“…Then, the clusters are obtained by assigning each data to its nearest center. The clustering process of the PFSOM algorithm is detailed in (Abidi and Ben Yahia 2013).…”

Section: Fuzzy Possibilistic Clustering For Hybrid Microaggregationmentioning

confidence: 99%

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Hybrid microaggregation for privacy preserving data mining

Abidi

Yahia

Perera

2018

J Ambient Intell Human Comput

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k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest tradeoff between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-PFSOM, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HM-PFSOM approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information.

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Section: Discussion and Motivationmentioning

confidence: 99%

Section: The Hm-pfsom Algorithm For Hybrid Microaggregationmentioning

confidence: 99%

Section: Fuzzy Possibilistic Clustering For Hybrid Microaggregationmentioning

confidence: 99%

“…Then, the clusters are obtained by assigning each data to its nearest center. The clustering process of the PFSOM algorithm is detailed in (Abidi and Ben Yahia 2013).…”

Section: Fuzzy Possibilistic Clustering For Hybrid Microaggregationmentioning

confidence: 99%

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