Homomorphic Encryption for Secure LLM Knowledge Distillation

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Solution Overview

Problem

Existing machine learning models deployed in cloud environments face privacy concerns during knowledge transfer operations due to vulnerabilities in communication between local and cloud models, and traditional encryption methods are computationally demanding, limiting their practical application in real-time systems.

Innovation Solution

A system utilizing homomorphic encryption to secure data processing by encrypting queries and responses, enabling knowledge distillation between local and external environments while maintaining data privacy and optimizing computational resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional encryption is used to protect data during communication, then data privacy is improved, but computational overhead increases substantially

Engineering Contradiction:

Improvedata privacyVSAvoidcomputational overhead

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent introduces homomorphic encryption as an intermediary mechanism that enables secure computation on encrypted data without requiring decryption. This allows the system to maintain data privacy during knowledge distillation while avoiding the substantial computational overhead of traditional encryption/decryption cycles for every operation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the encryption parameter from traditional symmetric/asymmetric encryption to homomorphic encryption, which fundamentally alters how data is protected during computation. This parameter change enables privacy preservation without the same computational penalties, as operations can be performed directly on encrypted data using specialized algorithms.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If homomorphic encryption is used to enable computation on encrypted data, then data privacy during processing is improved, but computational demands increase

Engineering Contradiction:

Improvedata privacy during processingVSAvoidcomputational demands

Core Design Contradiction:

ReliabilityVSPower

Solution Approach 1:

The patent segments the computational workload by performing knowledge distillation operations separately from the encrypted data processing. The local model performs distillation using decrypted data locally, while only necessary encrypted communications occur with the cloud model. This segmentation reduces the computational burden on any single component while maintaining privacy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial homomorphic encryption, using HE only for specific critical operations where privacy must be maintained, rather than encrypting all data and operations continuously. This partial application reduces overall computational demands while still providing privacy protection where most needed during knowledge transfer.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If a hybrid model with local and cloud models is used, then processing capability and privacy are improved, but communication vulnerability increases

Engineering Contradiction:

Improveprocessing capabilityVSAvoidcommunication vulnerability

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The patent uses homomorphic encryption as an intermediary protection layer for all communications between the local and cloud models. This ensures that even though the hybrid architecture requires communication between components, the data remains encrypted and protected during transmission, eliminating the communication vulnerability that would otherwise exist.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If knowledge distillation is performed continuously, then model capability is improved, but data exposure risk increases

Engineering Contradiction:

Improvemodel capabilityVSAvoiddata exposure risk

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The local model performs knowledge distillation autonomously using locally stored decrypted data, without requiring continuous communication with the cloud model. This self-service capability allows the model to improve its own capabilities while minimizing data exposure risks associated with continuous external communication and processing.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12506589B1System and method for secure knowledge distillation using homomorphic encryption in machine learning environments

Publication Date: 2025.12.23 CHAIN REACTION LTD

AI summary

A method for secure knowledge transfer between machine learning models is provided. The method includes receiving a query within a secure environment, determining whether a local language model within the secure environment is capable of processing the query based on one or more criteria, and generating a response using the local language model when the local language model is determined to be capable of processing the query. An external processing request is generated when the local language model is determined to be not capable of processing the query and the external processing request is encrypted using homomorphic encryption. The encrypted external processing request is transmitted to an external environment and an encrypted response from the external environment is received. The encrypted response is decrypted and knowledge distillation is performed on the decrypted response to extract knowledge patterns; and the local language model is updated using the extracted knowledge patterns.