TY - JOUR
T1 - Toward a comparison of classical and new privacy mechanism
AU - Heredia-Ductram, Daniel
AU - Nunez-del-Prado, Miguel
AU - Alatrista-Salas, Hugo
N1 - Publisher Copyright:
© 2021 by the authors.
PY - 2021/4/15
Y1 - 2021/4/15
N2 - 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.
AB - 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.
KW - Privacy
KW - Statistical disclosure control
KW - Generative adversary networks
KW - Differential privacy
KW - Knowledge distillation
UR - http://www.scopus.com/inward/record.url?scp=85104639508&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c59fb691-e4f6-31da-ab1a-fd2bcb27d65d/
U2 - 10.3390/e23040467
DO - 10.3390/e23040467
M3 - Article in a journal
SN - 1099-4300
VL - 23
JO - Entropy
JF - Entropy
IS - 4
M1 - 467
ER -