4 papers · 1 filter
Secure Aggregation in Federated Learning using Multiparty Homomorphic Encryption
Erfan Hosseini, Shuangyi Chen, Ashish Khisti
A key operation in federated learning is the aggregation of gradient vectors generated by individual client nodes. We develop a method based on multiparty homomorphic encryption (M…
Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption
Shuangyi Chen, Yue Ju, Zhongwen Zhu +1
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 ac…
SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
Shuangyi Chen, Ashish Khisti
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…
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning
Shuangyi Chen, Anuja Modi, Shweta Agrawal +1
Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to…