Hybrid Neural Network with Homomorphic Encryption for Client Deployment
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Solution Overview
Problem
The deployment of large neural networks on less secure client devices poses significant security risks due to the potential theft of valuable intellectual property, as the learned parameters can be inspected or copied, necessitating a secure execution environment.
Innovation Solution
A hybrid neural network architecture is implemented, where linear operations are performed on the client and non-linear operations on the server, with homomorphic encryption used to secure the client-side parameters, ensuring that computations can be performed on encrypted data without decryption, maintaining network security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are deployed on client devices, then computational capability and functionality are improved, but security risks and intellectual property theft increase
Solution Approach 1:
The neural network is divided into two distinct parts: a server-side component containing the trained parameters (weights and biases) and a client-side component performing linear operations. This segmentation allows the sensitive intellectual property to remain on the secure server while enabling functional deployment on client devices through encrypted computation.
Solution Approach 2:
Homomorphic encryption serves as an intermediary mechanism that enables computation on encrypted data without requiring decryption. The encryption layer acts as a mediator between the server's secure parameters and the client's computational resources, allowing functional interaction while maintaining security.
2Object-affected harmful factors
If parameters are encrypted on client devices, then security and intellectual property protection are improved, but computational complexity and processing requirements increase
Solution Approach 1:
The complex encryption processing is extracted from the client device and relocated to the server. The server performs the homomorphic encryption of parameters before transmission to the client, eliminating the need for clients to handle complex cryptographic operations while maintaining security.
Solution Approach 2:
Instead of requiring clients to store and process sensitive parameters, the system creates encrypted copies of the parameters that can be processed securely on the server and transmitted to clients for execution, separating the sensitive data from the computational burden.
3Object-affected harmful factors
If all neural network operations are performed on the server, then security is maintained, but computational resources and processing speed are limited
Solution Approach 1:
Computational operations are segmented between server and client: the server handles secure parameter storage and non-linear operations, while the client handles linear operations using encrypted parameters. This division enables parallel processing and improves overall computational throughput while maintaining security.
Solution Approach 2:
The system changes the state of parameters through homomorphic encryption, transforming them from plaintext to encrypted form. This allows parameters to be processed on client devices without exposing their actual values, enabling improved productivity while maintaining security through parameter state transformation.
Data Source
AI summary
The technology described herein is related to a hybrid neural network that divides operations of a neural network layer between a server and a client device. In an aspect, one or more liner operations of a neural network layer are performed on the client, while non-linear operations, such as an activation function, are performed on the server. In an aspect, the technology described herein maintains network security by encrypting portions of the client-side components. The encrypted portions may be learned values, which may also be described as learned parameters. In aspects, homomorphic encryption is used.