Encrypted LLM Layers for Private Data Access Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large Language Models (LLMs) face significant security concerns due to extensive data requirements and complex architectures, which can lead to unauthorized access and misuse of sensitive information during training and inference processes, necessitating robust encryption and access control mechanisms.
Innovation Solution
The system employs controlled gradient propagation and encryption techniques to secure LLMs, allowing hierarchical access control by encrypting sensitive data layers and limiting updates to only authorized users, using Partially Homomorphic Encryption (PHE) and Fully Homomorphic Encryption (FHE) to perform computations on encrypted data without decryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are trained on vast amounts of data to improve performance, then the model's capabilities are enhanced, but security risks increase due to exposure of sensitive information
Solution Approach 1:
The model is divided into multiple layers with different access levels. Public layers can be accessed by anyone, while private layers containing sensitive information require authentication. This segmentation allows the model to maintain high performance through comprehensive training data while mitigating security risks by restricting access to sensitive portions of the model.
Solution Approach 2:
An authentication mechanism serves as an intermediary between users and the private layers of the model. This intermediary verifies user credentials before allowing access to sensitive model components, enabling the model to process sensitive data securely without exposing it to unauthorized users.
2Object-affected harmful factors
If encryption is applied to secure model layers, then data confidentiality is improved, but computational complexity increases
Solution Approach 1:
Encryption is applied selectively to specific model layers rather than the entire model. Public layers remain unencrypted for computational efficiency, while only private layers containing sensitive information are encrypted. This local application of encryption maintains data confidentiality where needed while minimizing the computational complexity burden.
3Object-affected harmful factors
If gradient propagation is limited to specific layers during backpropagation, then access control is improved, but training efficiency decreases
Solution Approach 1:
The gradient propagation mechanism is made dynamic, adapting its behavior based on the training context. During normal training, gradients flow through all layers for optimal learning. During inference, the system dynamically restricts gradient propagation to only private layers when authentication fails, providing access control without permanently sacrificing training efficiency.
Data Source
AI summary
A system generates an MLM comprising a plurality of layers. The system assigns a first encryption scheme for a first subset of layers in the plurality of layers. During a training phase of the MLM, the system determines whether a first input training vector comprises private data, in response to determining that the first input training vector does not comprise the private data, the system train the MLM such that, during backpropagation, an optimization algorithm is used to update any necessary weights in the plurality of layers; and in response to determining that the first input training vector comprises the private data, the system trains the MLM such that during the backpropagation, the optimization algorithm is used to update weights solely in the first subset of layers. The system executes the trained MLM on a user input vector to generate a user output value.