5 papers
CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
Haiwei Lin, Shoko Imaizumi, Hitoshi Kiya
Privacy-preserving action recognition (PPAR) enables machines to understand human activities in videos without revealing sensitive visual content. Among the various strategies for…
FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters
Hiroto Sawada, Shoko Imaizumi, Hitoshi Kiya
In this paper, we propose a method for privacy-preserving federated learning that uses randomly selected model parameters to update global models. High-quality deep neural networks…
Privacy-Preserving Semantic Segmentation without Key Management
Mare Hirose, Shoko Imaizumi, Hitoshi Kiya
This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed method, the model creator and e…
On the Security of Bitstream-level JPEG Encryption with Restart Markers
Mare Hirose, Shoko Imaizumi, Hitoshi Kiya
This paper aims to evaluate the security of a bitstream-level JPEG encryption method using restart (RST) markers, where encrypted image can keep the JPEG file format with the same…
Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning
Hiroto Sawada, Shoko Imaizumi, Hitoshi Kiya
In this paper, we propose a novel method for enhancing security in privacy-preserving federated learning using the Vision Transformer. In federated learning, learning is performed…