Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs

TL;DR

This paper explores a novel framework for privacy-preserving pretraining of large language models using encrypted synthetic data, balancing privacy with model performance.

Contribution

It introduces an entity-based data synthesis method with deterministic encryption for PII, enabling privacy-preserving continual pretraining of LLMs.

Findings

Pretrained models outperform base models on limited datasets.

Encrypted models retain instruction-following capabilities.

Increasing entities and graph-based synthesis improves performance.

Abstract

Preserving privacy in sensitive data while pretraining large language models on small, domain-specific corpora presents a significant challenge. In this work, we take an exploratory step toward privacy-preserving continual pretraining by proposing an entity-based framework that synthesizes encrypted training data to protect personally identifiable information (PII). Our approach constructs a weighted entity graph to guide data synthesis and applies deterministic encryption to PII entities, enabling LLMs to encode new knowledge through continual pretraining while granting authorized access to sensitive data through decryption keys. Our results on limited-scale datasets demonstrate that our pretrained models outperform base models and ensure PII security, while exhibiting a modest performance gap compared to models trained on unencrypted synthetic data. We further show that increasing the number of entities and leveraging graph-based synthesis improves model performance, and that encrypted models retain instruction-following capabilities with long retrieved contexts. We discuss the security implications and limitations of deterministic encryption, positioning this work as an initial investigation into the design space of encrypted data pretraining for privacy-preserving LLMs. Our code is available at https://github.com/DataArcTech/SoE.