Partial Homomorphic Encryption in Distributed 1-Bit LLM Processing
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Solution Overview
Problem
Large Language Models (LLMs) face significant challenges due to high memory and processing requirements, limiting accessibility and posing security risks, particularly when handling private and sensitive data.
Innovation Solution
A 1-bit distributed LLM architecture is deployed across client devices and servers, using partial homomorphic encryption (PHE) to encrypt data operations on servers while performing operations locally on client devices, optimizing memory and processing efficiency while ensuring data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs are deployed with full encryption to ensure data security, then security is improved, but computational overhead and processing time increase significantly
Solution Approach 1:
The patent applies partial homomorphic encryption instead of full encryption, encrypting only specific data elements that require protection while leaving other operations in plaintext. This selective approach maintains security for sensitive data while avoiding the computational overhead of encrypting all operations, thus resolving the contradiction between security and processing efficiency
Solution Approach 2:
The system segments the LLM operations into encrypted and plaintext portions, with the client device handling plaintext operations and only transmitting encrypted data for sensitive operations. This segmentation allows the system to maintain security where needed while preserving processing efficiency for non-sensitive operations
2Ease of operation
If LLMs are deployed on resource-constrained devices to improve accessibility, then ease of operation is improved, but memory and processing requirements become problematic
Solution Approach 1:
The patent extracts the heavy computational and storage requirements from the client device and relocates them to remote servers. The client device retains only the essential interface functionality, while the actual LLM processing occurs on server infrastructure with sufficient resources, enabling accessibility without compromising memory requirements
Solution Approach 2:
The system introduces a server as an intermediary between the user and the LLM processing. The client device communicates with the server, which handles all resource-intensive operations including encrypted and plaintext processing. This intermediary architecture allows resource-constrained devices to access LLM capabilities without needing substantial local memory or processing power
3Productivity
If LLMs process vast amounts of data to improve model capabilities, then productivity is improved, but security risks increase due to potential data exposure
Solution Approach 1:
The system applies encryption selectively to only the data elements that require protection, rather than encrypting all processed data. This partial encryption approach maintains the ability to process vast amounts of data for model capabilities while protecting only the sensitive portions from exposure
Solution Approach 2:
The client device performs plaintext processing locally for non-sensitive operations, eliminating the need to transmit such data to servers. This self-service approach allows the system to process large volumes of data while keeping sensitive information local and unprotected data processed without exposure risk
Data Source
AI summary
A system determines whether a first operation performed by an MLM is compatible with a specific encryption scheme, wherein the MLM is distributed over at least one client device and at least one server. In response to determining that the first operation is compatible with the specific encryption scheme, the system encrypts data associated with the first operation using the specific encryption scheme, and transmits the encrypted data to the at least one server configured to apply the first operation. In response to determining that the first operation is incompatible with the specific encryption scheme, the system performs the first operation on the data using the at least one client device without encrypting using the specific encryption scheme.