SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy

arXiv:2404.16232v1 Announce Type: new
Abstract: In the context of prediction-as-a-service, concerns about the privacy of the data and the model have been brought up and tackled via secure inference protocols. These protocols are built up by using single or multiple cryptographic tools designed under a variety of different security assumptions.
In this paper, we introduce SECO, a secure inference protocol that enables a user holding an input data vector and multiple server nodes deployed with a split neural network model …

arxiv as-a-service context cryptographic cs.cr cs.dc data hierarchy prediction privacy protocols security server service single tools under