  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.
