Privacy
Research papers, repositories, and articles about privacy
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LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs
The authors separate two questions: can a model spit out training data, and how often does it actually do that in normal use. They build a framework that measures both worst-case extractability and everyday leakage. If you handle sensitive data, this is a blueprint for stress-testing your models instead of trusting vague privacy claims.
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
Fed-SE is a federated learning framework for LLM agents that must improve across heterogeneous environments under strict privacy constraints. It combines local parameter-efficient fine-tuning on high-return trajectories with global aggregation in a low-rank subspace, reducing negative transfer and boosting average success rates by ~18% over federated baselines. ([huggingface.co](https://huggingface.co/papers/2512.08870))