Encrypted Neural Network Inference via Homomorphic Encryption
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Solution Overview
Problem
Deep Learning (DL) models, particularly Neural Networks (NN), face challenges in protecting intellectual property (IP) when deployed on decentralized and potentially insecure systems, as existing solutions fail to secure the model, input data, and inference results effectively, making them vulnerable to reverse engineering.
Innovation Solution
The implementation of Fully Homomorphic Encryption (FHE) techniques to encrypt trained NN models, allowing secure deployment on decentralized systems while preserving model confidentiality and preventing reverse engineering, by encrypting parameters and enabling encrypted inference and fine-tuning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are deployed on decentralized systems for edge computing and IoT, then accessibility and practical utility are improved, but security and IP protection deteriorate due to vulnerability to reverse engineering
Solution Approach 1:
The patent applies preliminary action by encrypting the neural network model parameters before deployment on decentralized systems. The encryption is performed in advance using homomorphic encryption techniques, so that the model is already protected when it reaches the edge devices. This prevents attackers from obtaining plain-text models through reverse engineering, while still allowing the encrypted model to function correctly in the decentralized environment.
2Reliability
If traditional encryption methods are applied to protect neural network models, then IP protection is improved, but inference functionality deteriorates due to inability to process encrypted data
Solution Approach 1:
The patent replaces traditional encryption mechanisms with homomorphic encryption, which fundamentally changes how encryption works. Instead of requiring decryption before processing (traditional approach), homomorphic encryption allows mathematical operations to be performed directly on encrypted data. This substitution enables both IP protection and inference functionality to coexist, as the encrypted model can process encrypted inputs and produce encrypted outputs without ever being decrypted.
3Reliability
If neural network parameters are encrypted using homomorphic encryption, then model confidentiality is improved, but computational overhead increases
Solution Approach 1:
The patent applies parameter changes by optimizing the homomorphic encryption implementation for neural network workloads. This includes selecting appropriate encryption schemes (such as BFV or CKKS), adjusting security parameters, and optimizing the precision of encrypted computations. These parameter adjustments balance model confidentiality with computational efficiency, making the approach practical for real-world deployment on resource-constrained edge devices.
Data Source
AI summary
Systems and methods are provided for receiving input data to be processed by an encrypted neural network (NN) model, and encrypting the input data using a fully homomorphic encryption (FHE) public key associated with the encrypted NN model to generate encrypted input data. The systems and methods further provided for processing the encrypted input data to generate an encrypted inference output, using the encrypted NN model by, for each layer of a plurality of layers of the encrypted NN model, computing an encrypted weighted sum using encrypted parameters and a previous encrypted layer, the encrypted parameters comprising at least an encrypted weight and an encrypted bias, approximating an activation function for the level into a polynomial, and computing the approximated activation function on the encrypted weighted sum to generate an encrypted layer. The generated encrypted inference output is sent to a server system for decryption.