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Feb, 2020
关于使用梯度塑形来有效地减轻数据投毒攻击的效果
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
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Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, Nicolas Papernot
TL;DR
本文研究了机器学习算法对数据污染攻击的脆弱性,提出了一种基于梯度整形的攻击不可知防御方法,并评估了基于差分隐私随机梯度下降的可行性和有效性。研究结果表明,梯度整形是未来研究的有前途的方向。
Abstract
machine learning algorithms
are vulnerable to
data poisoning attacks
. Prior taxonomies that focus on specific scenarios, e.g., indiscriminate or targeted, have enabled defenses for the corresponding subset of kno
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