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Oct, 2019
随机梯度 langevin 动力学中的成员隐私特征
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
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Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan Yao...
TL;DR
本文从成员隐私保护的角度出发,研究了SGLD算法的信息泄露性质,证明了SGLD算法能够在一定程度上防止训练数据集的信息泄露,同时实验结果验证了该算法在实际应用中不仅能够减少信息泄露,还能够提高深度神经网络的泛化能力。
Abstract
bayesian deep learning
is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs).
stochastic gradient langevin dynamics
~(SGLD) is an effective method to enable
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