Trading-Off Privacy, Utility, and Explainability in Deep Learning-Based Image Data Analysis

Abstract

This paper proposes a novel approach for multi-party collaborative data analysis problems, where analysis accuracy and divergence are required, as well as both privacy of shared data and explainability of results. The proposed approach aims at trading-off data privacy, decision explainability, and data utility by analytically relating these three measures, evaluating how they impact each other, and proposing a methodology to find the best possible <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trade-off</i> among them. In particular, given a set of requirements from the participants for a collaborative analysis problem, we propose a method to properly tune the parameters of privacy-preserving mechanisms and explainability techniques to be adopted by all participants, obtaining the best <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trade-off</i> . The paper is focused on deep learning-based image data analysis problems, though the approach can be generalized to other data types. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon , \delta )$</tex-math></inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Differential Privacy</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Autoencoders</i> privacy-preserving techniques have been adopted to preserve data privacy, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothGrad</i> mechanism has been used to provide decision explainability. The proposed methodology has been validated with a set of experiments on three multi-class deep learning classifiers and three well-known image datasets, MNIST, FER, and CIFAR-10.

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