论文标题
具有不同隐私的模型解释
Model Explanations with Differential Privacy
论文作者
论文摘要
Black-Box机器学习模型用于关键决策域中，从而引起了几个算法透明度的呼吁。缺点是模型说明可以泄露有关培训数据的信息以及用于生成它们的说明数据，从而破坏了数据隐私。为了解决此问题，我们建议私人算法构建基于功能的模型说明。我们设计了一种自适应的差异性梯度下降算法，该算法找到了产生准确解释所需的最小隐私预算。它通过自适应重用过去的私人解释来减少解释数据的总体隐私损失。它还扩大了有关培训数据的隐私保证。我们评估了差异私人模型的含义以及我们的隐私机制对模型解释质量的影响。
Black-box machine learning models are used in critical decision-making domains, giving rise to several calls for more algorithmic transparency. The drawback is that model explanations can leak information about the training data and the explanation data used to generate them, thus undermining data privacy. To address this issue, we propose differentially private algorithms to construct feature-based model explanations. We design an adaptive differentially private gradient descent algorithm, that finds the minimal privacy budget required to produce accurate explanations. It reduces the overall privacy loss on explanation data, by adaptively reusing past differentially private explanations. It also amplifies the privacy guarantees with respect to the training data. We evaluate the implications of differentially private models and our privacy mechanisms on the quality of model explanations.
