On the use of fuzzy partitions to protect data

Abstract

Data protection is one of the most challenging tasks nowadays due to the huge amount of information that can be shared and crossed from different sources. Releasing microdata is a way to protect data, mainly in the economic and medical field. However, this kind of data can experience privacy attacks. This paper proposes the use of fuzzy sets as a way to improve the protection of privacy in microdata. Then, traditional definitions of k-anonymity, l-diversity and t-closeness are extended. The performance of these new approaches is checked in terms of the risk index.

Some Results and Applications Using Fuzzy Logic in Artificial Intelligence

“…Following this path, in [23,24] fuzzy notions were introduced in wellknown measures in the privacy framework as k-anonymity, l-diversity and t-closeness. These works propose the extension of these three measures when the data are protected using fuzzy sets instead of intervals or representative elements.…”

Recent advances in control algorithms for smart structures and machines

“…In recent years, researchers have tried to use artificial and computational intelligence concepts such as neural networks (Cabessa & Siegelmann, ; Donnarumma, Prevete, Chersi, & Pezzulo, ), fuzzy logic (Quirós Alonso, Díaz, & Montes, ), evolutionary computing (Cheng, Zhang, Caraffini, & Neri, ; Martínez‐Ballesteros Bacardit, & Riquelme, ), machine learning (Mesejo, Ibanez Enrique Fernandez‐Blanco, Cedron, Pazos, & Porto‐Pazos, ; You, Benitez‐Quiroz, & Martinez, ), and agent‐based modeling to develop adaptive or intelligent control algorithms.…”

Gravitational Search Algorithm and Its Variants

“…Ghasemi et al 28 proposed fuzzy GSA employing a fuzzy-based mechanism to extract a Pareto optimal solution as the best compromised solution based on the fuzzy set theory. 43,69 FGSA has been applied to achieve optimal tuning of power system stabilizers.…”