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- W2891555102 abstract "This paper considers the scenario that multiple data owners wish to apply amachine learning method over the combined dataset of all owners to obtain thebest possible learning output but do not want to share the local datasets owingto privacy concerns. We design systems for the scenario that the stochasticgradient descent (SGD) algorithm is used as the machine learning method becauseSGD (or its variants) is at the heart of recent deep learning techniques overneural networks. Our systems differ from existing systems in the followingfeatures: {bf (1)} any activation function can be used, meaning that noprivacy-preserving-friendly approximation is required; {bf (2)} gradientscomputed by SGD are not shared but the weight parameters are shared instead;and {bf (3)} robustness against colluding parties even in the extreme casethat only one honest party exists. We prove that our systems, whileprivacy-preserving, achieve the same learning accuracy as SGD and hence retainthe merit of deep learning with respect to accuracy. Finally, we conductseveral experiments using benchmark datasets, and show that our systemsoutperform previous system in terms of learning accuracies." @default.
- W2891555102 created "2018-09-27" @default.
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- W2891555102 date "2018-09-10" @default.
- W2891555102 modified "2023-09-26" @default.
- W2891555102 title "Privacy-Preserving Deep Learning for any Activation Function." @default.
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