CutClean: Pruning Neural Networks to Reduce Privacy Leakage

A recent paper published on arXiv (2608.13773) presents CutClean, a novel method for pruning neural networks with a focus on privacy. This technique seeks to minimize the transmission of private data through the network while enhancing its sparsity. The authors reveal that privacy breaches can happen even in the absence of conventional dataset biases, which poses significant risks in critical applications. CutClean incorporates auxiliary linear privacy heads at each block of the network to assess information leakage and implements varying levels of sparsity to eliminate the leakage of private attributes, evaluated by the accuracy of the privacy head on the final block. Tests on both synthetic and real-world datasets indicate that this method successfully reduces private information flow while maintaining high sparsity. The paper is classified as a cross announcement, and the authors remain unnamed. This research advances the domain of privacy-preserving machine learning by providing a strategy to bolster privacy without compromising model performance.