  Split Learning (SL) is a distributed learning framework renowned for its
privacy-preserving features and minimal computational requirements. Previous
research consistently highlights the potential privacy breaches in SL systems
by server adversaries reconstructing training data. However, these studies
often rely on strong assumptions or compromise system utility to enhance attack
performance. This paper introduces a new semi-honest Data Reconstruction Attack
on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to
prior works, FORA relies on limited prior knowledge, specifically that the
server utilizes auxiliary samples from the public without knowing any client's
private information. This allows FORA to conduct the attack stealthily and
achieve robust performance. The key vulnerability exploited by FORA is the
revelation of the model representation preference in the smashed data output by
victim client. FORA constructs a substitute client through feature-level
transfer learning, aiming to closely mimic the victim client's representation
preference. Leveraging this substitute client, the server trains the attack
model to effectively reconstruct private data. Extensive experiments showcase
FORA's superior performance compared to state-of-the-art methods. Furthermore,
the paper systematically evaluates the proposed method's applicability across
diverse settings and advanced defense strategies.
