Jan 1, 2024·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
With the rise of decentralized systems and applications that run over multiple blockchains, there is a growing need for architectures and bridges that ensure the trusted transfer of data and assets between the chains. zkBridge (ACM CCS'22) is a cross-chain bridge protocol that was proposed for non-permissioned blockchain and uses a network of relays, each working (for example as a light node) on a blockchain. A relay communicates with a corresponding smart contract on another chain to transfer data from the first chain to the second, and the smart contract on the second chain is used to verify the correctness of the received data from the first chain. zkBridge designs and implements optimized zero-knowledge proofs, that minimize the work of the smart contract on the second chain to verify the correctness of the received data. In this paper, we consider applications that work across two or more permissioned blockchains. We propose sigBridge that uses the framework of zkBridge but replaces the costly zero-knowledge proof computation with a pair of algorithms based on the consensus algorithm of the first chain. The algorithms will be run by a relay node on the first chain and a smart contract on the second chain and provide verifiability of data and asset transfer from the first chain to the second. The pair of algorithms are significantly more efficient compared to generating zero-knowledge proofs, verifying them, or running the full consensus algorithm of the first blockchain. We then show how a decentralized user-centric resource-sharing application will work over this architecture. We give a proof-of-concept implementation of an attribute-based access control system for a resource-sharing application that runs over two private Ethereum blockchains, and report the computation costs of the protocol.
The integration of blockchain technology into medical billing systems presents a transformative approach to addressing long-standing challenges in data security, transparency, and transaction integrity within healthcare finance. This quantitative study examines the implementation and performance of blockchain-enabled medical billing frameworks, focusing on their capacity to ensure immutability, traceability, and fraud prevention in electronic health payment transactions. By employing a mixed-method analytical model that integrates transaction latency metrics, cryptographic hash validation rates, and decentralized ledger consistency indices across 152 sampled healthcare billing networks, the study quantifies the impact of blockchain on billing accuracy and system resilience. Results indicate that blockchain-driven systems demonstrate a statistically significant 38% reduction in billing discrepancies and a 45% improvement in data reconciliation efficiency compared to traditional electronic billing infrastructures. Furthermore, smart contract automation within Ethereum-based frameworks reduced claim-processing times by up to 62%, mitigating human error and unauthorized modifications. The research also highlights how consensus algorithms, particularly Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT), enhance the verification process for medical transactions without compromising processing speed. These findings underscore blockchain’s potential to redefine financial trust structures within healthcare ecosystems by ensuring end-to-end transactional integrity, auditability, and compliance with data privacy regulations such as HIPAA and GDPR. The study concludes that blockchain-enabled secure billing systems can significantly strengthen institutional accountability and patient trust while establishing a robust foundation for interoperable, tamper-proof financial data management across global healthcare networks.
The rapid adoption of Internet of Things (IoT) devices in smart homes has led to growing security vulnerabilities, primarily due to the limitations of traditional, static access control mechanisms. This paper presents a novel, dynamic access control policy that leverages the immutable and transparent nature of Blockchain technology, specifically Ethereum, along with machine learning algorithms to enhance security measures. By integrating machine learning algorithms like Support Vector Machines (SVM) and Neural Networks, the proposed system can adapt and respond to changing behavioural patterns and potential threats in real time. Additionally, a caching mechanism implemented on the Ethereum Blockchain is introduced to optimize system performance and reduce latency. Experimental results demonstrate significant improvements in access control security, system efficiency, and adaptability. The findings of this paper not only contribute to the advancement of secure access control policies for IoT smart homes but pave the way for future research in integrating Blockchain and machine learning for robust and scalable IoT security solutions.
Blockchain is an emerging technology with Big data, Artificial Intelligence, and Machine Learning. It disrupted industries such as health, education, manufacturing, and banking. However, the increasing popularity of Blockchain ex- poses the scalability issues of major public blockchain platforms (e.g., Bitcoin and Ethereum) and dramatically affects its development. The scalability problem manifests in terms of Low throughput, high transaction latency, and massive energy consumption. Several reviews and studies cover these factors and their potential solutions, yet these studies need to highlight more information through actual application to natural systems or projects. This study investigates all relevant papers on current research solutions for public blockchain scalability issues. The scope of this paper is to explore the implementation of different state-of-the-art scalability solutions to natural systems and projects while simultaneously highlighting the results. This study discusses the methods and techniques used and the challenges encountered that have yet to future researchers must explore.
Traditional banking systems were significantly disrupted by the advent of digital currencies, most notably cryptocurrencies like Bitcoin and Ethereum, which have provided alternative ways of conducting transactions and storing value. Among other aspects, this research paper explores the far-reaching effects that digital currencies have had on traditional banking including disintermediation, payment systems, cross-border transactions and risk management. Besides, the paper examines the regulatory challenges posed by digital currencies through presenting different case studies from various jurisdictions and it also analyzes the technological integration of blockchain as well as distributed ledger technologies within mainstream banks’ frameworks. The next section discusses broader economic implications such as financial stability, monetary policy and consumer behavior. In addition, future prospects and challenges are looked at with strategic recommendations being given for traditional banks to transform themselves in order to survive under a changing financial landscape. Therefore this full analysis seeks to provide an understanding of how digital currencies can be transformational while suggesting a path for guiding old banks through new era of financial innovation.
Internet of Things (IoT) has more security issues due to the data being shared in an open platform. Integrating blockchain into IoT for security is a new development in computational communication systems. However, attackers are adapting their methods and creating new vulnerabilities in blockchain-based IoT platforms. Furthermore, when the blockchain is integrated with IoT networks, vulnerabilities, privacy issues, and security threats are amplified due to malicious transactions and active attacks. This paper proposes BlockDLO, an approach to IoT security that combines blockchain technology with deep learning. A five-phase architecture is proposed for the edge computing blockchain. In its first phase, network localization is resolved with chaotic map-based identification and authentication. The second phase proposes page rank-based clustering for edge computing. Then, BlockDLO combines the shared-chain technique with a deep distributed file system to address issues with block creation and ledger distribution, and an ethereum smart contract to address data security concerns. The communication route optimization is done with page rank centrality search optimization in its next phase. Finally, the integration of deep learning model to detect malicious data in the IoT network is done using the authenticated received data. BlockDLO creates an efficient intrusion detection system by combining a deep convolution neural network with blockchain. The proposed system is trained using public data sources and tested using an in-house network testbed. The results demonstrate that the proposed system outperforms existing work in terms of energy usage, packet loss rate, end-to-end delay, routing overhead, network lifetime, accuracy, and security strength.
Metaverse brings unlimited space and tremendous potential since it is an integrated application of multiple fundamental technologies such as artificial intelligence, blockchain, networking, Internet of Things, and interactivity. During those building blocks of metaverse, blockchain is a type of technology operated by a group of individual participants and known for its immutability feature. The massive adoption of blockchain has been severely prevented by various security and scalability issues in blockchain-based applications due to the inherent characteristics of this technology. To accelerate the massive adoption of blockchain, many previous studies have been carried out to address the security and scalability issues. This article reviews blockchain-related publications collected from four major security conferences (i.e., NDSS, CCS, S&P, and USENIX Security) published in the past three years. Through this overview, we disclose the security and scalability issues of mainstream blockchains such as Bitcoin and Ethereum. Our study aims to help researchers better understand the bottleneck of blockchain-empowered metaverse, and how to address user requirements for security and scalability from the perspective of blockchains.
Efficient sharing of comprehensive and relevant electronic health records (EHRs) is critical for precise disease research and high-quality, timely diagnoses. Recently, blockchain-based EHR sharing solutions have addressed issues such as single points of failure and data silos. However, decentralized solutions still face challenges in efficiently tracking and sharing dispersed and correlated medical records. Patients, lacking professional medical knowledge, struggle to construct and maintain the lineage of their overall medical records, leading to inefficient and insecure sharing of correlated EHRs with trusted doctors. Doctors from various departments also struggle to access the most relevant EHRs necessary for a precise understanding of patients’ medical histories. This study introduces a blockchain-based EHR system with provenance awareness, collecting data provenance to document traceable patient histories, facilitate rapid authorizations for correlated EHRs in a lineage, and conduct post-authorization audits. Designing a DAG-like data structure, the system efficiently stores the EHR provenance on blockchain and optimizes provenance-specific search. The system introduces a dedicated EHR authorization layer to facilitate dynamic authorization propagation along with the EHR provenance path. Harnessing Nash equilibrium principles, the system establishes an honesty-driven auditing mechanism to scrutinize doctors’ data access provenance and the intricate journey of patient health records. We developed a prototype using Ethereum blockchain smart contracts, verifying the feasibility of our model. The prototype successfully integrates data provenance, operates at expected costs, enhances user-friendliness, and improves access control efficiency.
The blockchain trilemma, introduced in 2017 on a blog post authored by Vitalik Buterin, one of Ethereum’s co-founders, asserts that achieving decentralization, scalability, and security simultaneously within a blockchain is unattainable. While this concept has garnered empirical support through extensive analyses of blockchain performance, it remains unproven theoretically. In this study, we establish a formula representing the trilemma within a Proof of Work blockchain and validate it through theoretical and experimental analyses. Additionally, we explore the correlation between a formula term denoting decentralization and established decentralization indices, finding a strong correlation with the Herfindahl-Hirschman Index. Moreover, our analysis reveals that strategies to enhance trilemma properties within the trilemma’s constraints can be classified into two distinct categories. The first strategy is to reduce the block header or transaction size. The second strategy is to optimize the propagation time per byte between nodes.
Aakanksha Bedi, J. Ramprabhakar, R. S. Anand, U Kumaran · 6 authors
Smart grids (SGs) are technology-powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the grid’s capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants, and lack of customer engagement due to various cyber-attacks. Such cyber-attacks will create distrust among consumers/prosumers to adopt the smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain-supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as the Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real-time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenarios, and the proposed algorithms are tested for power consumption and scalability, and results are presented. Moreover, blockchain simulation was first done in the local blockchain using Ganache and Truffle IDE and later using the Holesky Ethereum test network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.
This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus uncompressed data. The methodology encompasses rigorous statistical analysis to ensure robust results. Our findings reveal that using the Zlib algorithm to compress textual data exceeding 141 bytes before submitting transactions on the Ethereum network reduces the gasUsed while maintaining the system time unchanged. This demonstrates the effectiveness of data compression in optimizing transaction costs without affecting operational efficiency. Additionally, our research extends to analyzing real gasPrice trends on the Ethereum network. We propose a non-linear regression model that accurately predicts hourly gasPrice variations based on the day of the week and the specific time. This provides a valuable tool for users to plan transactions strategically. These insights enhance the understanding of blockchain dynamics and offer practical solutions for improving economic and system efficiency in blockchain operations.
Ruqaya Abdelmagid, Mohamed Abdelsalam, Fahad Kamal Alsheref
This paper proposes a framework to solve academic certificate fraud by implementing a blockchain network. A permission Hyperledger Fabric network is deployed to store students’ information and allows the proper access to guarantee the system's security. The paper discusses several studies that introduce variants of solutions for the academic certification tampering problem by using blockchain technology. It finds Hyperledger Fabric secure, and performant with higher TPS than Bitcoin and Ethereum; latency increases with participant number.
Blockchain’s promise of an immutable distributed ledger, fully fault tolerant with no centralized control deployed in an untrusted decentralized peer-to-peer network has disrupted the financial and technical industry. Blockchains may vary in terms of their design goals and algorithms, but they all rely on one common premise: the blockchain network is a decentralized peer-to-peer system and the ledger is stored on these untrusted peers. In this paper, we measure the characteristics of blockchain peers in the top four blockchains with dominant market share, Bitcoin, Ethereum, Ripple, and Solana. All four blockchains have a common trend that they have moved away from the idealistic decentralized peer-to-peer systems in their original design, and have become more server-like, more centralized, and more vulnerable to certain attacks. Using our measurements, we quantify the amount of effort required to delay, disrupt, and attack these blockchains.
Weiqi Dai, Jinkai Liu, Yang Zhou, Kim‐Kwang Raymond Choo · 7 authors
While blockchain is known to support open and transparent data exchange, partly due to its nontamperability property, it can also be (ab)used to facilitate the spreading of fake and misleading information or information that was subsequently discredited. Hence, this paper proposes a practical, redactable blockchain framework with a public trapdoor (hereafter referred to as PRBFPT). PRBFPT comprises an editing scheme for adding blocks using a new type of blockchain with a chameleon hash. Specifically, PRBFPT is able to involve all nodes in the blockchain in the editing operations by means of a public trapdoor, without requiring additional trapdoor management by predefined nodes or organizations. PRBFPT is also designed to audit and record the content of each editing operation. In other words, after editing and deleting the original data, PRBFPT can still verify its legitimacy. We also propose a contract-based locked voting scheme to better support voting. We then evaluate the prototype implementation of PRBFPT, whose findings show that the total time consumption of adding modules is at the millisecond level, with a negligible impact on the performance of the original system. In addition, the evaluation findings show that the cost of initiating the special transactions is comparable to the consumption of normal Ethereum transactions and is within a manageable range.
In this research, we analyzed three different models for Ethereum price prediction: a custom Hidden Markov Models (HMM), GHMM Pretrained, Deep Reinforcement Learning and LSTM. Our results demonstrate the distinct strengths and weaknesses of every model. Although HMM and HMM Pretrained excel in capturing volatility and short-term price fluctuations, the custom model demonstrates remarkable predictive capabilities for long-term trends. The present study provides significant contributions to the field of cryptocurrency price prediction, hence assisting traders, investors, and scholars in maneuvering through the complex Ethereum market.