Shoko Imaizumi

The paper introduces a novel privacy-preserving semantic segmentation method that enables model training and inference using independently encrypted images for each client and image.

The paper proposes FLRSP, a privacy-preserving federated learning method that enhances robustness by randomly selecting model parameters for global model updates, maintaining high accuracy against state-of-the-art attacks.

The paper proposes CFE-PPAR, the first compression-friendly encryption method for privacy-preserving action recognition, allowing video transformers to recognize actions directly from compressed, encrypted videos.