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- Token-level differential privacy for LLMs - Integration with Document Privacy API - Comprehensive test suite and documentation - Examples and Jupyter notebook included
1.3 KiB
1.3 KiB
Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
[Unreleased]
[0.1.0] - 2025-01-01
Added
- Initial release of DP-Fusion-Lib
DPFusionclass for differentially private text generation- Message-based context building with
add_message() - Direct context generation with
generate() - Token-level generation with
generate_from_tokens()
- Message-based context building with
Taggerclass for automatic private phrase extraction- Integration with Document Privacy API
- Support for multiple document types (HEALTH, FINANCE, LEGAL)
- Privacy accounting functions
compute_epsilon_single_group()for single-group privacy guaranteescompute_dp_epsilon()for multi-group scenarios
- Utility functions for advanced usage
compute_renyi_divergence_clipped_symmetric()for divergence computationfind_lambda()for binary search of mixing parameterreplace_sequences_with_placeholder_fast()for token-level redaction
- Support for HuggingFace transformers models
- Incremental decoding with KV-cache optimization
- Comprehensive documentation and examples
Dependencies
- PyTorch >= 2.0.0
- Transformers >= 4.25.0
- Requests >= 2.25.0