Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption

04/14/2021
by   Jing Ma, et al.
0

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between k<N-1 participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.

READ FULL TEXT
research
05/27/2023

Privacy-Preserving Model Aggregation for Asynchronous Federated Learning

We present a novel privacy-preserving model aggregation for asynchronous...
research
06/08/2023

FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users

The federated learning (FL) technique was developed to mitigate data pri...
research
12/10/2021

Federated Two-stage Learning with Sign-based Voting

Federated learning is a distributed machine learning mechanism where loc...
research
07/27/2020

VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

Due to the strong analytical ability of big data, deep learning has been...
research
07/21/2020

FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning

With the increasing awareness of privacy protection and data fragmentati...
research
09/16/2022

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation

Privacy has become a major concern in machine learning. In fact, the fed...
research
03/20/2023

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

Federated Learning (FL) enables machine learning model training on distr...

Please sign up or login with your details

Forgot password? Click here to reset