We propose a secure inference protocol for a distributed setting involving a single server node and multiple client nodes. We assume that the observed data vector is partitioned across multiple client nodes while the deep learning model is located at the server node. Each client node is required to encrypt its portion of the data vector and transmit the resulting ciphertext to the server node. The server node is required to collect the ciphertexts and perform inference in the encrypted domain. We demonstrate an application of multi-party homomorphic encryption (MPHE) to satisfy these requirements. We propose a packing scheme, that enables the server to form the ciphertext of the complete data by aggregating the ciphertext of data subsets encrypted using MPHE. While our proposed protocol builds upon prior horizontal federated training protocol~\cite{sav2020poseidon}, we focus on the inference for vertically partitioned data and avoid the transmission of (encrypted) model weights from the server node to the client nodes.

翻译：我们提出了一种针对分布式场景的安全推理协议，该场景包含一个服务器节点和多个客户端节点。假设观测数据向量被分区存储在多个客户端节点，而深度学习模型位于服务器节点。每个客户端节点需加密其持有的数据向量片段，并将生成的密文传输至服务器节点。服务器节点需收集这些密文并在加密域中执行推理。我们展示了多方同态加密（MPHE）在该需求中的应用，并提出一种打包方案，使服务器能够通过聚合使用MPHE加密的数据子集密文，构建完整数据的密文。虽然后续协议基于先前的横向联邦训练协议~\cite{sav2020poseidon}，但我们聚焦于垂直分区数据的推理，并避免了从服务器节点向客户端节点传输（加密）模型权重。