Homomorphic Encryption for Transformer Inference Privacy
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Solution Overview
Problem
Existing transformer networks lack encryption for user inputs, making them vulnerable to interception and unsuitable for sensitive applications like medicine, banking, or law, and existing encryption methods are challenging to apply to transformer inputs and outputs.
Innovation Solution
The implementation of homomorphic encryption, which allows computations to be performed on encrypted data without decrypting it, is applied to transformer networks. This involves generating homomorphically encrypted input embedding vectors, performing inferencing, and transmitting encrypted intermediate and output vectors between a server and client computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If transformer networks process user inputs without encryption, then processing speed and computational efficiency are improved, but data privacy and security deteriorate
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer between the user input and the transformer network processing. This encryption scheme allows the data to remain encrypted throughout the computation process while still enabling the transformer to perform necessary operations on the encrypted data, thus resolving the contradiction between processing efficiency and data security
Solution Approach 2:
The patent changes the state of the input data from plaintext to ciphertext through homomorphic encryption. This parameter change allows the data to maintain its computational properties while being encrypted, enabling the transformer network to process encrypted data without decrypting it, thus maintaining both processing capability and security
2Object-affected harmful factors
If existing encryption methods are applied to transformer inputs, then data privacy is improved, but computational complexity and implementation difficulty worsen
Solution Approach 1:
The patent replaces conventional encryption mechanisms with homomorphic encryption specifically designed for neural network computations. This substitution enables direct integration of encryption with transformer operations, reducing the need for complex encryption/decryption cycles and simplifying the overall implementation while maintaining data privacy
Solution Approach 2:
The patent makes the encryption scheme universal by designing it to work directly with transformer network operations. The homomorphic encryption scheme can accommodate various transformer operations (attention mechanisms, feed-forward layers, etc.) without requiring separate handling for each operation, thus reducing implementation complexity while providing comprehensive privacy protection
Data Source
AI summary
A server computing device is provided, including a processor configured to receive a homomorphically encrypted input embedding vector from a client computing device. At a transformer network, the processor may generate a plurality of homomorphically encrypted intermediate vectors at least in part by performing inferencing on the homomorphically encrypted input embedding vector. The processor may transmit the plurality of homomorphically encrypted intermediate output vectors to the client computing device. The processor may receive a plurality of homomorphically encrypted intermediate input vectors from the client computing device subsequently to transmitting the homomorphically encrypted intermediate output vectors to the client computing device. At the transformer network, the processor may generate a homomorphically encrypted output vector at least in part by performing additional inferencing on the homomorphically encrypted intermediate input vectors. The processor may transmit the homomorphically encrypted output vector to the client computing device.