Toward a Comparison of Classical and New Privacy Mechanism

Autor: Daniel Heredia-Ductram, Miguel Nunez-del-Prado, Hugo Alatrista-Salas
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Entropy, Vol 23, Iss 4, p 467 (2021)
Druh dokumentu: article
ISSN: 23040467
1099-4300
DOI: 10.3390/e23040467
Popis: In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather many data from different sources worldwide. This phenomenon has raised privacy concerns around the globe, compelling states to enforce data protection laws. In parallel, privacy-enhancing techniques have emerged to meet regulation requirements allowing companies and researchers to exploit individual data in a privacy-aware way. Thus, data curators need to find the most suitable algorithms to meet a required trade-off between utility and privacy. This crucial task could take a lot of time since there is a lack of benchmarks on privacy techniques. To fill this gap, we compare classical approaches of privacy techniques like Statistical Disclosure Control and Differential Privacy techniques to more recent techniques such as Generative Adversarial Networks and Machine Learning Copies using an entire commercial database in the current effort. The obtained results allow us to show the evolution of privacy techniques and depict new uses of the privacy-aware Machine Learning techniques.
Databáze: Directory of Open Access Journals
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