This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI), especially in the context of federated learning, for credit scoring in financial sectors to improve the credit assessment process. Research shows that integration of these cutting-edge technologies is in its infancy, specifically in the areas of embracing broader data, model verification, behavioural reliability and model explainability for intelligent credit assessment. The conventional credit risk assessment process utilises historical application data. However, reliable and dynamic transactional customer data are necessary for robust credit risk evaluation in practice. Therefore, this research proposes a framework for integrating blockchain and XAI to enable automated credit decisions. The main focus is on effectively integrating multi-party, privacy-preserving decentralised learning models with blockchain technology to provide reliability, transparency, and explainability. The proposed framework can be a foundation for integrating technological solutions while ensuring model verification, behavioural reliability, and model explainability for intelligent credit assessment.

Blockchain technology is becoming a promising technological solution for enterprise applications with the rise of interoperable solutions. A cross-chain architecture facilitates interoperability, thus improves its chain efficiency, reduces fragmentation, and allows users and features to flow more freely across multiple blockchains. However, enabling interoperability in silo networks will make a significant functional trade-off on the security and performance of the system. This paper review trade-offs in blockchain technologies related to interoperability.

Peer-to-Peer (P2P) energy trading platforms are being actively designed, tested, and operated by engineers, power distribution companies, and prosumers. The assurance of the accountability of the conduct of different stakeholders through a robust trust management mechanism is imperative in such platforms. The usage of blockchain, as an underlying technology, can ensure numerous properties, such as immutability, transparency, and traceable execution of transactions, in addition to ensuring trust establishment among different entities of the system. Few blockchain-based decentralized energy trading platforms have been designed in the literature to build trust about the platform and among prosumers. However, none of these proposals have considered human-in-the-loop in the trust establishment process. Moreover, these solutions have considered trust only at a particular layer of the blockchain, such as at the application or consensus layer. To bridge this gap, this article presents a novel cross-layer trust-based consensus protocol that considers human-in-the-loop and employs fuzzy logic to address the issue of vagueness of trust values by offering human interpretable trust level. The experimental results demonstrate the efficiency and effectiveness of our proposed protocol in comparison to established consensus mechanisms. The analysis also shows the protocol is immune against selfish mining, 51% and Sybil attacks.

The future of multi-blockchain architecture depends on the emergence of new protocols that achieve communication between trustless cross-chain participants. However, interoperability between blockchains remains an open problem. Existing approaches provide integration through solutions using a middleware system, which makes it harder to gain confidence mainly in terms of security and correctness of the process. A cross-chain protocol needs to provide a self-verifiable state-proof that embeds trust in the transfer process. We propose a Burn-to-Claim cross-chain protocol to seamlessly exchange assets between networks. Our scheme transfers assets from one blockchain system to another in a way that the asset is burned from the source blockchain and claimed on the destination blockchain. Our mechanism employs combinations of crypto mechanisms such as digital signatures and time lock to operate the protocol in a distributed manner. We provide an analysis which proves that our cross-chain protocol transfers assets correctly and securely.

Wearable devices continuously produce physiological data that can provide individuals critical information about their daily routine or fitness level in combination with their smartphones without requiring manual calculations or maintaining log-books. Real-time participant-generated data can enable large scale observational studies of health conditions, provide better insights into medical conditions of individuals and streamline clinical trial processes in medical research. However, privacy is a major concern for health data and there can be a lack of trust among different parties in the health data collection process. In addition, individuals often do not have sufficient control over the sharing of their data from the wearable devices. The lack of control, trust and privacy are key barriers to research participants being prepared to share their personal data from wearable devices. In this work, we propose a trust model to overcome the trust deficit among different parties. Then, we present a reference system architecture, rooted on the developed trust model, that provides incentive for individuals to securely share their health data through a data marketplace. By encouraging individuals to share their real-time health data, researchers will have access to large data sets at low cost.

Wireless Sensor Network (WSN) consists of a large number of small sensor nodes (SNs) that are usually deployed in hostile environments. These nodes are vulnerable to a number of security attacks and it is difficult to implement complex cryptographic schemes in SNs due to their resource constrained nature. Hence, WSNs need energy efficient and secure communication schemes in terms of storage space, power consumption and operation speed. In this work, we propose a secure and lightweight encryption scheme based on chaotic map and genetic operations. We also present some initial results of security strength and performance evaluation experiments.

In this paper we have proposed an energy efficient clique based clustering and routing scheme for wireless sensor networks. Developing energy efficient routing protocol is one of the major challenges in wireless sensor networks. Since the Sensor Nodes (SN) are small-sized battery operated devices, energy conservation is considered a critical factor in maximizingnetwork lifetime. Our proposed routing protocol partitions sensor networks into several disjoint cliques such that each node is in single hop distance from all of its neighbour nodes. At the beginning of the clustering process, each node obtains a list of its neighbours' connectivity as well as their degree of connection. Then, the node with the highest degree of connection initiates clique formation process and makes the cluster. Among the members of a cluster, the node with the maximum energy is selected as cluster head. Cluster head rotation depends on threshold value, which is obtained by averaging residual energy of all the nodes in a cluster and then making it half. Simulation results with MATLAB show that our proposed protocol is effective in terms of network lifetime as well as total energy dissipation.