Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems

This PhD dissertation concludes a three-year long research journey on the integration of Fog Computing and Blockchain technologies. The main aim of such integration is to address the challenges of each of these technologies, by integrating it with the other. Blockchain technology (BC) is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. It was initially proposed for decentralized cryptocurrency applications with practically proven high robustness. Fog Computing (FC) is a geographically distributed computing architecture, in which various heterogeneous devices at the edge of network are ubiquitously connected to collaboratively provide elastic computation services. FC provides enhanced services closer to end-users in terms of time, energy, and network load. The integration of FC with BC can result in more efficient services, in terms of latency and privacy, mostly required by Internet of Things systems

Consistency Analysis of Distributed Ledgers in Fog-Enhanced Blockchains

Both revolutionary technologies of Fog Computing (FC) and Blockchain (BC) serve as enablers for enhanced, people-centric trusted applications, and they do meet in the provision of higher standards and expectations. In this paper, we address the reliability of fog-enhanced BC systems by analyzing the forking phenomenon under different conditions, and provide a reliable Distributed Ledger (DL) consistency assessment. We use the FoBSim tool that is specifically designed to mimic and emulate realistic FC-BC integration, in which we deploy the Proof-of-Work (PoW) consensus algorithm and analyze the forking probability under fluctuating conditions. Based on our results, we propose an inconsistency formula, which can quantitatively describe how consistent the DL in a BC system can be. Finally, we show how to deploy this formula in a decision making model for indicating optimal deployment features of a BC network in a Fog-enhanced system. © 2022, Springer Nature Switzerland AG

Approaches to Overpower Proof-of-Work Blockchains Despite Minority

Blockchain (BC) technology has been established in 2009 by Nakamoto, using the Proof-of-Work (PoW) to reach consensus in public permissionless networks (Praveen et al., 2020). Since then, several consensus algorithms were proposed to provide equal (or higher) levels of security, democracy, and scalability, yet with lower levels of energy consumption. However, Nakamoto's model (a.k.a. Bitcoin) still dominates as the most trusted model in the described sittings since alternative solutions might provide lower energy consumption and higher scalability, but they would always require deviating the system towards unrecommended centralization or lower levels of security. That is, Nakamoto's model claims to tolerate (up to) < 50% of the network being controlled by a dishonest party (minority), which cannot be realized in alternative solutions without sacrificing the full decentralization property. In this paper, we investigate this tolerance claim, and we review several approaches that can be used to undermine/overpower PoW-based BCs, even with minority. We discuss those BCs taking Bitcoin as a representative application, where needed. However, the presented approaches can be applied in any PoW-based BC. Specifically, we technically discuss how a dishonest miner in minority, can take over the network using improved Brute-forcing, AI-assisted mining, Quantum Computing, Sharding, Partial Pre-imaging, Selfish mining, among other approaches. Our review serves as a needed collective technical reference (concluding more than 100 references), for practitioners and researchers, who either seek a reliable security implementation of PoW-based BC applications, or seek a comparison of PoW-based, against other BCs, in terms of adversary tolerance

PriFoB: A Privacy-aware Fog-enhanced Blockchain-based system for Global Accreditation and Credential Verification

Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential management solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DSs) within a Fog–Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL), and provide a ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of a Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms. © 2022 The Author(s

DONS: Dynamic Optimized Neighbor Selection for smart blockchain networks

Blockchain (BC) systems mainly depend on the consistent state of the Distributed Ledger (DL) at different logical and physical places of the network. The majority of network nodes need to be enforced to use one or both of the following approaches to remain consistent: (i) to wait for certain delays (i.e. by requesting a hard puzzle solution as in PoW and PoUW, or to wait for random delays as in PoET, etc.) (ii) to propagate shared data through shortest possible paths within the network. The first approach may cause higher energy consumption and/or lower throughput rates if not optimized, and in many cases these features are conventionally fixed. Therefore, it is preferred to enhance the second approach with some optimization. Previous works for this approach have the following drawbacks: they may violate the identity privacy of miners, only locally optimize the Neighbor Selection method (NS), do not consider the dynamicity of the network, or require the nodes to know the precise size of the network at all times. In this paper, we address these issues by proposing a Dynamic and Optimized NS protocol called DONS, using a novel privacy-aware leader election within the public BC called AnoLE, where the leader anonymously solves the The Minimum Spanning Tree problem (MST) of the network in polynomial time. Consequently, miners are informed about the optimum NS according to the current state of network topology. We analytically evaluate the complexity, the security and the privacy of the proposed protocols against state-of-the-art MST solutions for DLs and well known attacks. Additionally, we experimentally show that the proposed protocols outperform state-of-the-art NS solutions for public BCs. Our evaluation shows that the proposed DONS and AnoLE protocols are secure, private, and they acutely outperform all current NS solutions in terms of block finality and fidelity. © 2021 The Author(s

Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, we can find many other application fields in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a private permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance with the metrics of reading and writing response latency. We found that the local Indy ledger reads 30–50% faster, and writes 65–85% faster than the Indy ledger running on the Google Cloud Platform

A Survey on Blockchain-Fog Integration Approaches

Fog computing (FC) is the extension of Cloud Computing (CC), from the core of the internet architecture to the edge of the network, with the aim to perform processes closer to end-users. This extension is proven to enhance security, and to reduce latency and energy consumption. Blockchain (BC), on the other hand, is the base technology behind crypto-currencies, yet is implemented in wide range of different applications. The security and reliability, along with the distributed trust management criteria proposed in BC, excited the research community to integrate it with FC, in a step towards reaching a distributed and trusted, Data, Payment, Reputation, and Identity management systems. In this survey we present the up-to-date state-of-the-art of FC-BC integration with a detailed literature review and classification. We discuss and categorize the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture. Our research presents detailed observations, analysis, and open challenges for the BC-FC integration. We believe such conclusions may clarify the vision of the BC-FC integration, and calibrate the compass towards open issues and future research directions. © 2013 IEEE