Author, as appears in the article.: Blanco-Justicia, Alberto; Sanchez, David; Domingo-Ferrer, Josep; Muralidhar, Krishnamurty
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Blanco Justicia, Alberto / Domingo Ferrer, Josep / Sánchez Ruenes, David
Keywords: Machine learning Federated learning Differential privacy Data utility machine learning federated learning data utility
Abstract:
We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the
ex ante
privacy guarantees of DP. Instead, what they deliver is basically noise addition similar to the traditional (and often criticized) statistical disclosure control approach. Due to the lack of formal privacy guarantees, the actual level of privacy offered must be experimentally assessed
ex post
, which is done very seldom. In this respect, we present empirical results showing that standard anti-overfitting techniques in ML can achieve a better utility/privacy/efficiency tradeoff than DP.
Thematic Areas: Theoretical computer science Medicina ii Matemática / probabilidade e estatística Interdisciplinar General computer science Engenharias iv Computer science, theory & methods Computer science (miscellaneous) Computer science (all) Ciência da computação Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: alberto.blanco@urv.cat david.sanchez@urv.cat josep.domingo@urv.cat
Author identifier: 0000-0002-1108-8082 0000-0001-7275-7887 0000-0001-7213-4962
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://dl.acm.org/doi/10.1145/3547139
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Acm Computing Surveys. 55 (8): 1-16
APA: Blanco-Justicia, Alberto; Sanchez, David; Domingo-Ferrer, Josep; Muralidhar, Krishnamurty (2023). A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning. Acm Computing Surveys, 55(8), 1-16. DOI: 10.1145/3547139
Article's DOI: 10.1145/3547139
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications