Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Beyond oracles – a critical look at real-world blockchains

This thesis intends to provide answers to the following questions: 1) What is the oracle problem, and how do the limitations of oracles affect different real-world applications? 2) What are the characteristics of the portion of the literature that leaves the oracle problem unaddressed? 3) Who are the main contributors to solving the oracle problem, and which issues are they focusing on? 4) How can the oracle problem be overcome in real-world applications? The first chapter aims to answer the first question through a literature review of the most current papers published in the field, bringing clarity to the blockchain oracle problem by discussing its effects in some of the most promising real-world blockchain applications. Thus, the chapter investigates the sectors of Intellectual Property Rights (IPRs), healthcare, supply chains, academic records, resource management, and law. By comparing the different applications, the review reveals that heterogeneous issues arise depending on the sector. The analysis supports the view that the more trusted a system is, the less the oracle problem has an impact. The second chapter presents the results of a systematic review intended to highlight the state-of-the-art of real-world blockchain applications using the oracle problem as a lens of analysis. Academic papers proposing real-world blockchain applications were reviewed to see if the authors considered the oracle’s role in the applications and related issues. The results found that almost 90% of the inspected literature neglected the role of oracles, thereby proposing incomplete or irreproducible projects. Through a bibliometric analysis, the third chapter sheds light on the institutions and authors that are actively contributing to the literature on oracles and promoting progress and cooperation. The study shows that, although there is still a lack of collaboration worldwide, there are dedicated authors and institutions working toward a similar and beneficial cause. The results also make it clear that most areas of oracle research are poorly addressed, with some remaining untouched. The fourth and last chapter focuses on a case study of a dairy company operating in the northeast region of Italy. The company applied blockchain technology to support the traceability of their products worldwide, and the study investigated the benefits of their innovation from the point of view of sustainability. The study also considers the role of oracle management, as it is a critical aspect of a blockchain-based project. Thus, the relationship between the company, the blockchain oracle, and the supervising authority is discussed, offering insight into how sustainable innovations can positively impact supply chain management. This work as a whole aims to shed light on blockchain oracles as an academic area of research, explaining why the study of oracles should be considered the backbone of blockchain literature development

Secure data sharing and processing in heterogeneous clouds

The extensive cloud adoption among the European Public Sector Players empowered them to own and operate a range of cloud infrastructures. These deployments vary both in the size and capabilities, as well as in the range of employed technologies and processes. The public sector, however, lacks the necessary technology to enable effective, interoperable and secure integration of a multitude of its computing clouds and services. In this work we focus on the federation of private clouds and the approaches that enable secure data sharing and processing among the collaborating infrastructures and services of public entities. We investigate the aspects of access control, data and security policy languages, as well as cryptographic approaches that enable fine-grained security and data processing in semi-trusted environments. We identify the main challenges and frame the future work that serve as an enabler of interoperability among heterogeneous infrastructures and services. Our goal is to enable both security and legal conformance as well as to facilitate transparency, privacy and effectivity of private cloud federations for the public sector needs. © 2015 The Authors

Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version