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Found 4 papers, 2 papers with code
Extracting Training Data from Large Language Models
We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data.
Making the Shoe Fit: Architectures, Initializations, and Tuning for Learning with Privacy
Because learning sometimes involves sensitive data, standard machine-learning algorithms have been extended to offer strong privacy guarantees for training data.
Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility
Machine learning (ML) research has investigated prototypes: examples that are representative of the behavior to be learned.
A General Approach to Adding Differential Privacy to Iterative Training Procedures
In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to training algorithms, provides a variety of configuration strategies for the privacy mechanism, and then isolates and simplifies the critical logic that computes the final privacy guarantees.
