相关论文: The Forking Way: When TEEs Meet Consensus
Blockchain and distributed ledger technologies rely on distributed consensus algorithms. In recent years many consensus algorithms and protocols have been proposed; most of them are for permissioned blockchain networks. However, the…
Collaborative cybersecurity relies on organizations sharing information to boost security, but trust management is a key concern. Decentralized solutions like distributed ledgers, particularly blockchain, are crucial for eliminating single…
Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work)…
Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face…
Edge computing brings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept of edge federation has recently emerged as a key mechanism for dynamic resources and services…
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that…
With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel…
Trusted Execution Environment (TEE) enhances the security of mobile applications and cloud services by isolating sensitive code in the secure world from the non-secure normal world. However, TEE applications are still confronted with…
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central…
In recent years, many Blockchain based frameworks for transacting commodities on a congestible network have been proposed. In particular, as the number of controllable grid connected assets increases, there is a need for a decentralized,…
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally.…
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most…
In cross-device private federated learning, differentially private follow-the-regularized-leader (DP-FTRL) has emerged as a promising privacy-preserving method. However, existing approaches assume a semi-honest server and have not addressed…
Cryptocurrencies are poised to revolutionize the modern economy by democratizing commerce. These currencies operate on top of blockchain-based distributed ledgers. Existing permissionless blockchain-based protocols offer unparalleled…
Trusted Execution Environments (TEEs) have emerged as a cornerstone for securing sensitive computations by providing isolated enclaves protected from untrusted software. However, their security guarantees are undermined by vulnerabilities…
Consensus algorithms are central to blockchain technology and an emerging research area. In this paper, we begin with an overview of the different types and architectures of blockchain networks. Then, with a focus on consortium blockchains,…
In the last few years, a countless number of permissioned blockchain solutions have been proposed, with each one to claim that it revolutionizes the way of the transaction processing along with the security and privacy preserving mechanisms…
Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in…
Blockchain is a growing decentralized system built for transparency and immutability. There have been several major attacks on blockchain-based systems, leaving a gap in the trustability of this system. This article presents a comprehensive…
Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain…