A look inside of homomorphic encryption for federated learning

Abstract

When you think of different standards of encryption you may think of Data Encryption Standard, Advanced Encryption Standard or Elliptic Curve Cryptography. However, a new standard for encryption, called homomorphic encryption, is being researched and put into use. Homomorphic encryption is a cryptographic technique that has the potential to significantly impact the field of Artificial Intelligence (AI). It allows data to be processed in an encrypted form without first decrypting it, thus preserving privacy and security while still enabling meaningful computation. Homomorphic encryption can also be applied in federated learning, a decentralized approach to machine learning. Multiple parties can collaborate to train a machine learning model without sharing their individual data directly. Throughout this paper first we will discuss what homomorphic encryption is and then, we explore how homomorphic encryption can be used to ensure that data remains encrypted during model updates and aggregation, enhancing privacy.