Split learning and differential privacy are technologies with growing
potential to help with privacy-compliant advanced analytics on distributed
datasets. Attacks against split learning are an important evaluation tool and
have been receiving increased research attention recently. This work's
contribution is applying a recent feature space hijacking attack (FSHA) to the
learning process of a split neural network enhanced with differential privacy
(DP), using a client-side off-the-shelf DP optimizer. The FSHA attack obtains
client's private data reconstruction with low error rates at arbitrarily set DP
epsilon levels. We also experiment with dimensionality reduction as a potential
attack risk mitigation and show that it might help to some extent. We discuss
the reasons why differential privacy is not an effective protection in this
setting and mention potential other risk mitigation methods.

本论文旨在探讨对分布式数据集进行隐私合规高级分析时，分割学习和差分隐私这两项技术的应用。通过使用最近的特征空间劫持攻击对增强差分隐私的分割神经网络的学习过程进行攻击，我们发现差分隐私并不是很有效的保护方法，并提出了一些潜在的风险缓解方法。