Fusion: Efficient and Secure Inference Resilient to Malicious Server and Curious Clients. (arXiv:2205.03040v1 [cs.CR])

In secure machine learning inference, most current schemes assume that the
server is semi-honest and honestly follows the protocol but attempts to infer
additional information. However, in real-world scenarios, the server may behave
maliciously, e.g., using low-quality model parameters as inputs or deviating
from the protocol. Although a few studies consider the security against the
malicious server, they do not guarantee the model accuracy while preserving the
privacy of both server's model and the client's inputs. Furthermore, a curious
client …

clients fusion malicious server