Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.

Managing identity is a crucial issue, and with the advent of digital identity, Self-Sovereign Identity (SSI) has been highlighted. SSI allows the owner to have sovereignty over their identity information. Currently, blockchain technology is widely used to implement SSI management system. Issuing a verifiable credential (VC) based on blockchain has two advantages: it ensures the VC has not been tampered with and makes the VC trustworthy by sharing the same ledger among untrusted parties. This paper presents a SSI management system which was designed and implemented for police or firefighters who still manage identity information centrally. Furthermore, given the inherent risks associated with these occupations, it is imperative to have a secure healthcare system to meet their specific needs. We propose a system that links these healthcare systems, allowing owners of identity information to monitor how their identity information has been used. The usage history of the system is stored in a permissioned blockchain, specifically Hyperledger Fabric.

As blockchain protocols exhibit diverse characteristics and performances, it is crucial to decide whether to develop a custom blockchain protocol, select an existing platform, or identify a promising protocol before initiating a blockchain-based service or starting a new business. To assist the general public and business operators in assessing the desirability of each blockchain protocol, various services provide evaluation and ranking services for blockchain projects. However, most of these services rely on qualitative evaluation based on reports provided by the blockchain development team. Therefore, we propose a quantitative evaluation method for blockchain protocols that compares and analyzes the technological status, future development direction, and technological differences of Layer 1 and Layer 2 protocols. Our approach incorporates indicators that are distinct from other services and indicators specific to layer 2 solutions, facilitating a more comprehensive analysis of blockchain protocols. This allows for objective evaluation of protocol performance and mutual comparison between protocols.

In accordance with the guidelines of the Financial Action Task Force (FATF), Virtual Asset Service Providers (VASPs) should comply with a ‘travel rule’, which requires them to exchange originator’s and beneficiary’s personal information when transferring virtual assets. In this paper, we propose a novel blockchain-based travel rule compliance system that supports fully-decentralized data exchange. The proposed system uses a permissioned blockchain, and thereby eliminates the possibility of leakage of personal information to third parties or even to travel rule service providers, and ensures that travel rule data can be managed securely.

In remote sensing, numerous unlabeled images are continuously accumulated over time, and it is difficult to annotate all the data. Therefore, a self-supervised learning technique that can improve the recognition rate using unlabeled data will be useful for remote sensing. This letter presents contrastive self-supervised learning with smoothed representation for remote sensing based on the SimCLR framework. In self-supervised learning for remote sensing, the well-known characteristic that images within a short distance might be semantically similar is usually used. Our algorithm is based on this knowledge, and it simultaneously utilizes several neighboring images as a positive pair of the anchor image, unlike existing methods such as Tile2Vec. Furthermore, MoCo and SimCLR, which are among the state-of-the-art self-supervised learning approaches, only use two augmented views of the single-input image, but our proposed approach uses multiple-input images and averages their representations (e.g., smoothed representation). Consequently, the proposed approach outperforms state-of-the-art self-supervised learning methods, such as Tile2Vec, MoCo, and SimCLR, in the cropland data layer (CDL), RESISC-45, UCMerced, and EuroSAT data sets. The proposed approach is comparable to the pretrained ImageNet model in the CDL classification task.