  Two-party split learning is a popular technique for learning a model across
feature-partitioned data. In this work, we explore whether it is possible for
one party to steal the private label information from the other party during
split training, and whether there are methods that can protect against such
attacks. Specifically, we first formulate a realistic threat model and propose
a privacy loss metric to quantify label leakage in split learning. We then show
that there exist two simple yet effective methods within the threat model that
can allow one party to accurately recover private ground-truth labels owned by
the other party. To combat these attacks, we propose several random
perturbation techniques, including $\texttt{Marvell}$, an approach that
strategically finds the structure of the noise perturbation by minimizing the
amount of label leakage (measured through our quantification metric) of a
worst-case adversary. We empirically demonstrate the effectiveness of our
protection techniques against the identified attacks, and show that
$\texttt{Marvell}$ in particular has improved privacy-utility tradeoffs
relative to baseline approaches.
