【Abstract】Blockchain is a disruptive technology, normally used within financial applications, however, it can be very beneficial in certain robotic contexts, such as when an immutable register of events is required. Among the several properties of Blockchain that can be useful within robotic environments, we find not just immutability but also data decentralization, irreversibility, accessibility and non-repudiation. In this paper, we propose an architecture that uses blockchain as a ledger, and smart-contracts for robotic control by using oracles to process data. We show how to register events in a secure way, how it is possible to use smart-contracts to control robots and how to interface with external algorithms for image analysis. The proposed architecture is modular and can be used in multiple contexts such as in manufacturing, network control, robot control, and others, since it is easy to integrate, adapt, maintain and extend to new domains, only requiring new tailored smart-contracts.
【Abstract】With the rapid development of digital currencies in recent years, their anonymity provides a natural shelter for criminals. This problem resulting in various types of malicious transactions emerge in an endless stream, which seriously endangers the financial order of digital currencies. Many researchers have started to focus on this area and have proposed heuristics and feature-based centralized machine learning algorithms to discover and identify malicious transactions. However, these approaches ignore the existence of financial flows between digital currency transactions and do not use the important neighborhood relationships and rich transaction characteristics. In addition, centralized learning exposes a large amount of transaction feature data to the risk of leakage, where criminals may trace the actual users using traceability techniques. To address these issues, we proposes a graph neural network model based on federated learning named GraphSniffer to identify malicious transactions in the digital currency market. GraphSniffer leverages federated learning and graph neural networks to model graph-structured Bitcoin transaction data distributed at different worker nodes, and transmits the gradients of the local model to the server node for aggregation to update the parameters of the global model. GraphSniffer can realize the joint identification and analysis of malicious transactions while protecting the security of transaction feature data and the privacy of the model. Extensive experiments validate the superiority of the proposed method over the state-of-the-art.
