Context-Preserving Tensor Anonymization for Private Transformer Training
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Solution Overview
Problem
Transformer models require raw text data for fine-tuning, which poses privacy and security risks due to the need for data download and encryption, and existing encryption methods are computationally burdensome and reversible, violating client privacy.
Innovation Solution
A two-step training approach that vectorizes raw text into irreversible tensors at the client side and fine-tunes a pretrained model on these vectors in a secure environment, leveraging the irreversibility of multi-head self-attention layers in transformer models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encryption methods are used to protect sensitive data during model fine-tuning, then data privacy is improved, but computation cost and system complexity increase
Solution Approach 1:
The patent extracts the sensitive data processing step from the centralized training environment and performs it locally at the client side. The embedding layer processes raw sensitive documents locally and transforms them into contextualized embeddings before transmission, eliminating the need for centralized encryption/decryption systems while maintaining data privacy.
Solution Approach 2:
The patent introduces contextualized embeddings as an intermediary representation between raw sensitive data and the centralized training process. This intermediary form preserves the semantic information needed for training while removing the ability to reconstruct the original sensitive data, thus acting as a privacy-preserving mediator.
2Manufacturing precision
If raw text data is downloaded for fine-tuning transformer models, then model performance is improved, but data security and privacy risks increase
Solution Approach 1:
The patent performs the embedding transformation action beforehand at the client side before any data leaves the local environment. The raw sensitive documents are transformed into contextualized embeddings locally, and only these transformed representations are transmitted for centralized fine-tuning, preventing any exposure of raw sensitive data.
Solution Approach 2:
Instead of the conventional approach where raw data is sent to the server and processed, the patent inverts the process: raw data remains local, only the processed embeddings are transmitted. This reversal of the data flow direction eliminates the security risk of data download while maintaining the ability to fine-tune models centrally.
3Reliability
If encryption and decryption systems are implemented for data protection, then privacy is preserved, but computation cost increases
Solution Approach 1:
The patent replaces the cryptographic encryption/decryption mechanism with a neural network-based embedding transformation. Instead of using mathematical encryption functions that require computationally intensive operations, the system uses the pretrained transformer's embedding layer to transform data, leveraging existing model computations without additional cryptographic overhead.
4Ease of operation
If reversible embedding methods are used to transform text into vectors, then model training is enabled, but data privacy is violated due to potential reconstruction
Solution Approach 1:
The patent performs the embedding transformation action beforehand at the client side before any data leaves the local environment. The raw sensitive documents are transformed into contextualized embeddings locally, and only these transformed representations are transmitted for centralized fine-tuning, preventing any exposure of raw sensitive data.
Data Source
AI summary
Systems and methods for privacy-preserving transformer model training are provided. The system includes one or more data repositories in a computer network or cloud infrastructure having data stored therein. The system anonymizes the data in the one or more documents, and trains a transformer model on the data outside of the network. The data includes sensitive information. Anonymizing the data is includes extracting the data from the one or more documents and irreversibly transforming the data in the one or more documents into context-preserving tensors. Training the transformer model on the data comprises using the context-preserving tensors instead of the data to train the transformer model on the data.