A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning

Author:

Blanco-Justicia Alberto1ORCID,Sánchez David1ORCID,Domingo-Ferrer Josep1ORCID,Muralidhar Krishnamurty2ORCID

Affiliation:

1. Universitat Rovira i Virgili, Tarragona, Catalonia

2. University of Oklahoma, Norman, OK

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.

Funder

European Commission

Norwegian Research Council

Government of Catalonia

UK Research and Innovation

UE

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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