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arXiv · 2502.20629

Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud

Griffin Higgins·Roozbeh Razavi-Far·Xichen Zhang·Amir David·Ali Ghorbani·Tongyu Ge
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Abstract

This work aims to provide both privacy and utility within a split learning framework while considering both forward attribute inference and backward reconstruction attacks. To address this, a novel approach has been proposed, which makes use of class activation maps and autoencoders as a plug-in strategy aiming to increase the user's privacy and destabilize an adversary. The proposed approach is compared with a dimensionality-reduction-based plug-in strategy, which makes use of principal component analysis to transform the feature map onto a lower-dimensional feature space. Our work shows that our proposed autoencoder-based approach is preferred as it can provide protection at an earlier split position over the tested architectures in our setting, and, hence, better utility for resource-constrained devices in edge-cloud collaborative inference (EC) systems.

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Griffin Higgins, Roozbeh Razavi-Far, Xichen Zhang, Amir David, Ali Ghorbani, Tongyu Ge. 2025-02-28. Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud. https://arxiv.org/abs/2502.20629

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