论文标题
消除不同私有随机梯度下降对模型准确性的不同影响
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
论文作者
论文摘要
当我们在机器学习中执行差异性隐私时，公用事业私人关系权衡是不同的W.R.T.每个组。梯度剪辑和随机噪声添加不成比例地影响了代表性不足和复杂的类别和亚组，这导致公用事业损失的不平等。在这项工作中，我们通过差异隐私分析了效用损失的不平等，并提出了一个经过修改的私有随机梯度下降（DPSGD），称为DPSGD-F，以消除差异隐私对受保护组的潜在不同影响。 DPSGD-F根据组剪切偏差来调整小组中样本的贡献，以使差异隐私对组公用事业没有不同的影响。我们的实验评估表明，组样本量和组剪切偏见如何影响DPSGD差异隐私的影响，以及每个组的自适应剪辑如何有助于减轻DPSGD-F差异隐私引起的不同影响。
When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r.t. each group. Gradient clipping and random noise addition disproportionately affect underrepresented and complex classes and subgroups, which results in inequality in utility loss. In this work, we analyze the inequality in utility loss by differential privacy and propose a modified differentially private stochastic gradient descent (DPSGD), called DPSGD-F, to remove the potential disparate impact of differential privacy on the protected group. DPSGD-F adjusts the contribution of samples in a group depending on the group clipping bias such that differential privacy has no disparate impact on group utility. Our experimental evaluation shows how group sample size and group clipping bias affect the impact of differential privacy in DPSGD, and how adaptive clipping for each group helps to mitigate the disparate impact caused by differential privacy in DPSGD-F.
