Privacy-protected aggregation in federated learning based on semi-homomorphic encryption

Haifeng Lin; Chen Chen; Yunfan Hu

doi:10.1117/12.2685483

21 July 2023 Privacy-protected aggregation in federated learning based on semi-homomorphic encryption

Haifeng Lin, Chen Chen, Yunfan Hu

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171J (2023) https://doi.org/10.1117/12.2685483
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

As people pay more and more attention to privacy protection, federated learning with privacy protection ability comes into being, and gradually becomes one of the research hotspots in the field of machine learning. As a distributed machine learning framework, federated learning can prevent data from being directly leaked, but some private information can still be derived from the gradient. Therefore, some cryptographic schemes are used to solve this privacy leakage. In this paper, Paillier algorithm in the semi-homomorphic encryption scheme is introduced to encrypt the gradient uploaded by clients in federated learning, which is aggregated by the server and sent back to each client for model update. Relevant experiments are also designed to verify the efficiency of Paillier algorithm through the change of the computing cost with the increase of the number of customers.

Citation Download Citation

Haifeng Lin, Chen Chen, and Yunfan Hu "Privacy-protected aggregation in federated learning based on semi-homomorphic encryption", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171J (21 July 2023); https://doi.org/10.1117/12.2685483

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available