Secure MPC Fine-Tuning for Private Language Model Training
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Solution Overview
Problem
Existing methods for fine-tuning language models on sensitive data degrade model accuracy and violate privacy, especially in scenarios where data cannot be shared due to regulatory constraints or lack of computing resources, necessitating a privacy-preserving solution.
Innovation Solution
Implementing a secure multiparty computation (MPC) protocol that allows decentralized fine-tuning of language models using additive secret sharing and optimized MPC-friendly designs, reducing runtime and network traffic while preserving model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional differential privacy approaches are used to protect sensitive training data, then privacy is preserved, but model accuracy significantly degrades
Solution Approach 1:
The patent introduces secure multi-party computation (MPC) as an intermediary mechanism that enables collaborative fine-tuning of language models across multiple organizations without directly sharing sensitive data. The MPC protocol acts as a mediator that computes gradients and updates model parameters while keeping each organization's data private, thus preserving both privacy and model accuracy without the need for differential privacy noise addition.
Solution Approach 2:
The patent segments the training process into separate gradient computation steps performed by each organization locally, then aggregates these gradients through MPC protocols. This segmentation allows each organization to contribute to model improvement without exposing their sensitive data, avoiding the accuracy degradation caused by differential privacy while maintaining privacy protection.
2Reliability
If data is decentralized across multiple organizations, then privacy is maintained, but training efficiency and model fine-tuning capability are reduced
Solution Approach 1:
The patent merges the computational capabilities of multiple decentralized organizations through secure multi-party computation protocols. By combining gradient computations from multiple organizations while maintaining data separation, the system achieves training efficiency comparable to centralized approaches while preserving data privacy and enabling collaborative fine-tuning of large language models.
Solution Approach 2:
MPC protocols serve as intermediaries that enable efficient aggregation of gradients from decentralized organizations without requiring data centralization. This intermediary mechanism maintains training efficiency by allowing parallel gradient computations to be combined securely, thus improving productivity while maintaining the privacy benefits of decentralized data architecture.
3Measurement precision
If sensitive data is shared for fine-tuning, then model accuracy improves, but privacy and security of training data are compromised
Solution Approach 1:
The patent uses secure multi-party computation as an intermediary that enables model fine-tuning without direct data sharing. The MPC protocol allows organizations to contribute gradients and participate in model improvement while preventing direct exposure of sensitive training data, thus achieving model accuracy improvement without the harmful effect of data exposure.
Solution Approach 2:
Instead of sharing original sensitive data, the patent uses copies in the form of encrypted gradients and aggregated updates through MPC protocols. Each organization works with local copies of their data to compute gradients, which are then combined securely without revealing the underlying sensitive information, thus improving model accuracy while avoiding data exposure risks.
Data Source
AI summary
Systems and methods for implementing a secure multiparty protocol for fine-tuning of language models are disclosed. An end-to-end privacy-preserving protocol using secure multi-party computation (MPC) and executed on a plurality of computing nodes enables fine-tuning a language model targeting classification tasks using private, sensitive data while providing secure protection of the training data and without sacrificing model accuracy.