标题： 分裂发生：使用分裂学习和函数秘密共享对抗高级威胁 显示英文标题

标题： Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

摘要： 分割学习（SL）——将模型分成两个不同部分，以帮助保护客户端数据，同时增强机器学习（ML）过程。 尽管有前景， SL已被证明容易受到各种攻击，从而引发了对其在数据隐私方面有效性的问题的担忧。 最近的研究通过使用一种名为函数秘密共享（FSS）的新范式，在服务器获得他们计算的函数的份额并操作一个用随机掩码隐藏的公共输入方面展示了有希望的结果。 然而，这些工作在应对SL存在的攻击数量增加方面存在不足。 在SplitHappens中，我们将FSS和SL的结合扩展到U型SL。 与其他工作类似，我们能够通过减少FSS的通信和计算成本来利用SL的优势。 然而，U型SL提供了比之前工作更高的安全保证，使客户端能够在不与服务器共享的情况下保持训练数据的标签机密性。 通过这种方式，我们能够推广之前工作的安全分析，并将其扩展到不同的攻击向量，例如现代模型逆向攻击以及标签推断攻击。 我们在不同数据集上的两种不同的卷积神经网络上测试了我们的方法。 这些实验表明，与简单地使用FSS相比，我们的方法在减少训练时间以及通信成本方面是有效的，同时保持了先前的准确性。 显示英文摘要

摘要： Split Learning (SL) -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning (ML) processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have shown promising results for securing SL through the use of a novel paradigm, named Function Secret Sharing (FSS), in which servers obtain shares of a function they compute and operate on a public input hidden with a random mask. However, these works fall short in addressing the rising number of attacks which exist on SL. In SplitHappens, we expand the combination of FSS and SL to U-shaped SL. Similarly to other works, we are able to make use of the benefits of SL by reducing the communication and computational costs of FSS. However, a U-shaped SL provides a higher security guarantee than previous works, allowing a client to keep the labels of the training data secret, without having to share them with the server. Through this, we are able to generalize the security analysis of previous works and expand it to different attack vectors, such as modern model inversion attacks as well as label inference attacks. We tested our approach for two different convolutional neural networks on different datasets. These experiments show the effectiveness of our approach in reducing the training time as well as the communication costs when compared to simply using FSS while matching prior accuracy.