19 citations · 28 across the 9 of their papers we have counts for
12 papers
Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images
Yuki Itabashi, Hiroto Sawada, Mare Hirose +2
Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from s…
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…
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications
Hitoshi Kiya, AprilPyone MaungMaung, Yuma Kinoshita +2
This article presents an overview of image transformation with a secret key and its applications. Image transformation with a secret key enables us not only to protect visual infor…
Application of Reversible Data Hiding for Printing with Special Color Inks to Preserve Compatibility with Normal Printing
Kotoko Hiraoka, Kensuke Fukumoto, Takashi Yamazoe +4
We propose an efficient framework with compatibility between normal printing and printing with special color inks in this paper. Special color inks can be used for printing to repr…
Reversible Data Hiding Associated with Digital Halftoning That Allows Printing with Special Color Ink by Using Single Color Layer
Minagi Ueda, Shoko Imaizumi
We propose an efficient framework of reversible data hiding to preserve compatibility between normal printing and printing with a special color ink by using a single common image.…
A Reversible Data Hiding Method in Compressible Encrypted Images
Shoko Imaizumi, Yusuke Izawa, Ryoichi Hirasawa +1
We propose a reversible data hiding (RDH) method in compressible encrypted images called the encryption-then-compression (EtC) images. The proposed method allows us to not only emb…
A Block-Permutation-Based Encryption Scheme with Independent Processing of RGB Components
Shoko Imaizumi, Hitoshi Kiya
This paper proposes a block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling. A BPBE image can be obta…
Grayscale-based Block Scrambling Image Encryption for Social Networking Services
Warit Sirichotedumrong, Tatsuya Chuman, Shoko Imaizumi +1
This paper proposes a new block scrambling encryption scheme that enhances the security of encryption-then-compression (EtC) systems for JPEG images, which are used, for example, t…