Toward a comparison of classical and new privacy mechanism

Daniel Heredia-Ductram, Miguel Nunez-del-Prado, Hugo Alatrista-Salas

Research output: Contribution to journalArticle in a journalpeer-review

Abstract

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.
Original languageEnglish
Article number467
Number of pages21
JournalEntropy
Volume23
Issue number4
DOIs
StatePublished - 15 Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors.

Keywords

  • Privacy
  • Statistical disclosure control
  • Generative adversary networks
  • Differential privacy
  • Knowledge distillation

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