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Found 3 papers, 1 papers with code
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
In this paper, we present Pencil, the first private training framework for collaborative learning that simultaneously offers data privacy, model privacy, and extensibility to multiple data providers, without relying on the non-colluding assumption.
LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers
The community explored to build private inference frameworks for transformer-based large language models (LLMs) in a server-client setting, where the server holds the model parameters and the client inputs its private data (or prompt) for inference.
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test
This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.
