Blockchain-Research and Applications最新文献

Federated learning (FL) allows data owners to train neural networks together without sharing local data, allowing the industrial Internet of Things (IIoT) to share a variety of data. However, traditional FL frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposes a dual-blockchain based multi-layer grouping federated learning (BMFL) architecture. BMFL divides the participant groups based on the training tasks, then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.

The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity and enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. This paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.

Blockchain is a type of distributed ledger technology that consists of a growing list of records, called blocks, that are securely linked together using cryptography. Each blockchain-based solution deploys a specific consensus algorithm that guarantees the consistency of the ledger over time. The most famous, and yet claimed to be the most secure, is the Proof-of-Work (PoW) consensus algorithm. In this paper, we revisit the fundamental calculations and assumptions of this algorithm, originally presented in the Bitcoin white paper. We break down its claimed calculations in order to better understand the underlying assumptions of the proposal. We also propose a novel formalization model of the PoW mining problem using the Birthday paradox. We utilize this model to formalize and analyze partial pre-image attacks on PoW-based blockchains, with formal analysis that confirms the experimental results and the previously proposed implications. We build on those analyses and propose new concepts for benchmarking the security of PoW-based systems, including Critical Difficulty and Critical Difficulty per given portion. Our calculations result in several important findings, including the profitability of launching partial pre-image attacks on PoW-based blockchains, once the mining puzzle difficulty reaches a given threshold. Specifically, for any compromised portion of the network ($q<0.5$; honest majority assumption still holds), the attack is formally proven profitable once the PoW mining puzzle difficulty reaches 56 leading zeros.

The sharing of pathological data is highly important in various applications, such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data poses significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.