论文标题
Nopeek：减少信息泄漏以在分布式深度学习中共享激活
NoPeek: Information leakage reduction to share activations in distributed deep learning
论文作者
论文摘要
对于使用敏感数据的分布式机器学习，我们演示了如何最大程度地减少原始数据和中介表示之间的距离相关性，从而减少了跨客户通信的敏感原始数据模式的泄漏，同时保持模型的准确性。泄漏（使用输入和中间表示之间的距离相关性测量）是与中间表示原始数据的可逆性相关的风险。这可以防止持有敏感数据的客户实体使用分布式深度学习服务。我们证明我们的方法对这种重建攻击具有弹性，并且基于在训练和推断图像数据集中的原始数据和学会表示之间的距离相关性的降低。我们可以防止对原始数据进行这种重建，同时维护维持良好分类精度所需的信息。
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient to such reconstruction attacks and is based on reduction of distance correlation between raw data and learned representations during training and inference with image datasets. We prevent such reconstruction of raw data while maintaining information required to sustain good classification accuracies.
