Journal ArticleDOI
Protecting data: a fuzzy approach
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TLDR
This work proposes to extend k-anonymity, l-diversity and t-closeness when the data are protected using fuzzy sets instead of intervals or representative elements to show an improvement in protecting data when data are encoded using fuzzy set.Abstract:
Privacy issues represent a longstanding problem nowadays. Measures such as k-anonymity, l-diversity and t-closeness are among the most used ways to protect released data. This work proposes to extend these three measures when the data are protected using fuzzy sets instead of intervals or representative elements. The proposed approach is then tested using Energy Information Authority data set and different fuzzy partition methods. Results shows an improvement in protecting data when data are encoded using fuzzy sets.read more
Citations
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Journal ArticleDOI
An entropy measure definition for finite interval-valued hesitant fuzzy sets
TL;DR: From this definition, several results have been developed for each mapping that shapes the entropy measure in order to get such functions with ease, and as a consequence, allowing to obtain this new entropy in a simpler way.
Journal ArticleDOI
On cardinalities of finite interval-valued hesitant fuzzy sets
TL;DR: In this work, the cardinality of finite interval-valued hesitant fuzzy sets is studied from an axiomatic point of view, together with several properties that enable it to relate to the classical definitions of cardinality given by other authors for fuzzy sets.
Journal ArticleDOI
On δ-ϵ-Partitions for Finite Interval-Valued Hesitant Fuzzy Sets
TL;DR: This work presents a partitioning method for the so-called finite interval-valued hesitant fuzzy sets, i.e, finitely generated sets, as well as the definitions of t-norm and t-conorm for these kinds of sets.
Book ChapterDOI
Some Results and Applications Using Fuzzy Logic in Artificial Intelligence
TL;DR: This chapter aims to highlight some recent works that are connected with the topic of this book, both in the theoretical and in the applied fields.
References
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Journal ArticleDOI
L-diversity: Privacy beyond k-anonymity
TL;DR: This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
Proceedings ArticleDOI
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
TL;DR: T-closeness as mentioned in this paper requires that the distribution of a sensitive attribute in any equivalence class is close to the distributions of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t).
Journal ArticleDOI
Protecting respondents identities in microdata release
TL;DR: This paper addresses the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer and introduces the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity.
Journal ArticleDOI
Achieving k -anonymity privacy protection using generalization and suppression
TL;DR: This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
Journal ArticleDOI
A new approach to clustering
TL;DR: A new method of representation of the reduced data, based on the idea of “fuzzy sets,” is proposed to avoid some of the problems of current clustering procedures and to provide better insight into the structure of the original data.