A Two-Levels Data Anonymization Approach - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2020

A Two-Levels Data Anonymization Approach

Abstract

The amount of devices gathering and using personal data without the person’s approval is exponentially growing. The European General Data Protection Regulation (GDPR) came following the requests of individuals who felt at risk of personal privacy breaches. Consequently, privacy preservation through machine learning algorithms were designed based on cryptography, statistics, databases modeling and data mining. In this paper, we present two-levels data anonymization methods. The first level consists of anonymizing data using an unsupervised learning protocol, and the second level is anonymization by incorporating the discriminative information to test the effect of labels on the quality of the anonymized data. The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.
Fichier principal
Vignette du fichier
497040_1_En_8_Chapter.pdf (303.76 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04050603 , version 1 (29-03-2023)

Licence

Attribution

Identifiers

Cite

Sarah Zouinina, Younès Bennani, Nicoleta Rogovschi, Abdelouahid Lyhyaoui. A Two-Levels Data Anonymization Approach. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.85-95, ⟨10.1007/978-3-030-49161-1_8⟩. ⟨hal-04050603⟩
23 View
26 Download

Altmetric

Share

Gmail Facebook X LinkedIn More