Overview of Federal Learning and Privacy Protection
Year: 2024
Type: article
Abstract: In recent years, federated learning technology has developed rapidly and been widely used in the field of data processing. This paper makes a comprehensive discussion on the privacy protection methods in federated learning,and makes a detailed analysis of three basic methods:data encryption,data perturbation and trusted hardware-based,introduces the principle of each method,and objectively analyzes their performance in practical applications. This paper gives a visual example to compare the principle of secure multi-party computing, which is easy for readers to understand.Aiming at homorphic, this paper first introduces the background of this method and explains the algorith with block diagram,and then compares and analyzes the advantages and disadvantages of somewhat homorphic encryption and fully homorphic encryption. The specific denoising mechanisms are divided into three types:Laplace mechanism,Gauss mechanism and exponential mechanism.This paper summarizes these three mechanisms according to the types of data that need to be denoised.Finally,the paper systematically expounds the privacy protection methods based on trusted hardware ,lists two typical schemes,TrustZone and SGX,and analyzes how they work. In this paper,the hot spots and development prospects are prospected.
DOI: 10.62051/m8pzzr79
Author: Ziyue Tian
Institution: Northeast Petroleum University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401552843