  Training deep neural networks often requires large-scale datasets,
necessitating storage and processing on cloud servers due to computational
constraints. The procedures must follow strict privacy regulations in domains
like healthcare. Split Learning (SL), a framework that divides model layers
between client(s) and server(s), is widely adopted for distributed model
training. While Split Learning reduces privacy risks by limiting server access
to the full parameter set, previous research has identified that intermediate
outputs exchanged between server and client can compromise client's data
privacy. Homomorphic encryption (HE)-based solutions exist for this scenario
but often impose prohibitive computational burdens.
  To address these challenges, we propose CURE, a novel system based on HE,
that encrypts only the server side of the model and optionally the data. CURE
enables secure SL while substantially improving communication and
parallelization through advanced packing techniques. We propose two packing
schemes that consume one HE level for one-layer networks and generalize our
solutions to n-layer neural networks. We demonstrate that CURE can achieve
similar accuracy to plaintext SL while being 16x more efficient in terms of the
runtime compared to the state-of-the-art privacy-preserving alternatives.
