Secure transactions using mobile agents with TTP

Electronic commerce has pushed and benefited from the development of mobile agents technology. One of the reasons is electronic commerce needs remote searching and negotiating between one customer and a number of E-shops. Mobile agents can travel over the Intranet or Internet. Therefore, mobile agents can help the customer or E-shops with remote searching and negotiating. However, because of the heterogeneousness of the networks the mobile agents migrate to, security issues should be tackled with cautions. This paper presents a new secure electronic commerce protocol. The underlying transactions are accomplished with mobile agents. A trusted third party (in fact, a trusted authority) is involved in the protocol

An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT)

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

E-Voting in an ubicomp world: trust, privacy, and social implications

The advances made in technology have unchained the user from the desktop into interactions where access is anywhere, anytime. In addition, the introduction of ubiquitous computing (ubicomp) will see further changes in how we interact with technology and also socially. Ubicomp evokes a near future in which humans will be surrounded by “always-on,” unobtrusive, interconnected intelligent objects where information is exchanged seamlessly. This seamless exchange of information has vast social implications, in particular the protection and management of personal information. This research project investigates the concepts of trust and privacy issues specifically related to the exchange of e-voting information when using a ubicomp type system