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Life sciences · Preprint
arXiv · August 13, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint describing CutClean, a neural network pruning technique that uses auxiliary privacy heads to measure and reduce private attribute information flow while maintaining model sparsity and accuracy. The work is a technical contribution to privacy-preserving machine learning methods but has not undergone peer review and does not report quantitative comparisons with existing approaches or detailed empirical metrics.
Preprint. Intervention: CutClean: a pruning method employing auxiliary linear privacy heads at each network block to quantify and reduce private attribute information leakage through increasing sparsity levels..
The method achieves high sparsity rates while preserving classification accuracy Privacy information flow is minimized as measured by privacy head accuracy on the last block The approach works on both synthetic and real-world datasets
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is an unrefereed preprint presenting a novel neural network pruning method for privacy; it demonstrates a technical approach on synthetic and real-world datasets but lacks peer review and clinical or established-outcome validation.
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Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of representation imbalances that lead to traditional dataset biases. This poses significant privacy risks when deploying models that process sensitive attributes. In this context, we propose CutClean, a privacy-aware pruning method that allows to reduce privacy information flow through the network, while increasing its sparsity. Our approach employs auxiliary linear privacy heads placed at each network's block to quantify information leakage, and further applies increasing levels of sparsity to remove the private attribute leakage, measured in terms of the accuracy of the privacy head attached to the last block. Experiments on synthetic and real-world datasets demonstrate that our approach effectively minimizes private information flow while achieving high sparsity rates and preserving classification target accuracy.
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