论文标题
功能分区的协作学习中的后门攻击和防御
Backdoor attacks and defenses in feature-partitioned collaborative learning
论文作者
论文摘要
由于协作学习中有多个政党，因此恶意政党可能会通过后门攻击来操纵学习过程。但是，大多数现有作品仅考虑联合学习方案，其中数据被样本划分。特征分区的学习可能是另一个重要的情况，因为在许多现实世界应用中，特征通常分布在不同的各方。当攻击者没有标签并且防守者无法访问其他参与者的数据和模型参数时，在这种情况下的攻击和防御尤其具有挑战性。在本文中，我们表明，即使是无法获得标签的各方也可以成功地注入后门攻击，从而在主要和后门任务上都达到了高精度。接下来，我们介绍了几种防御技术，表明可以通过这些技术的组合成功阻止后门，而不会损害主要任务准确性。据我们所知，这是第一次在特征分区的协作学习框架中处理后门攻击的系统研究。
Since there are multiple parties in collaborative learning, malicious parties might manipulate the learning process for their own purposes through backdoor attacks. However, most of existing works only consider the federated learning scenario where data are partitioned by samples. The feature-partitioned learning can be another important scenario since in many real world applications, features are often distributed across different parties. Attacks and defenses in such scenario are especially challenging when the attackers have no labels and the defenders are not able to access the data and model parameters of other participants. In this paper, we show that even parties with no access to labels can successfully inject backdoor attacks, achieving high accuracy on both main and backdoor tasks. Next, we introduce several defense techniques, demonstrating that the backdoor can be successfully blocked by a combination of these techniques without hurting main task accuracy. To the best of our knowledge, this is the first systematical study to deal with backdoor attacks in the feature-partitioned collaborative learning framework.
