Blockchain has toured the technological world as a revolutionary platform powering safe and secured storing of information across many sectors including digital currency and supply systems. This paper discusses the potential of this technology and the essential role it plays in the management of data integrity and security in distributed systems. It starts with a brief discussion of the key concepts of blockchain namely decentralization, the ledger is not controlled by the parties to the transaction or by any third party, immutability of records on the blockchain, the records on a blockchain cannot be tampered with once they have been put on the blockchain, and transparency, everybody can see the record but cannot influence it. The paper then looks at how these principles improve data security and data integrity as compared to centralized approach. Besides, it also looks at some of the drawbacks and possible limitations of blockchain about data consistency and protection again also giving scenarios that support its efficiency.
This study explores the development of a blockchain-based e-certificate system designed for secure and transparent credential management at the Muhammadiyah University of Gresik. Leveraging private blockchain technology, the system addresses the challenges of certificate forgery and unauthorized alterations by ensuring data immutability and integrity. Through the implementation of a distributed ledger, the platform facilitates seamless issuance, verification, and storage of e-certificates. The methodology encompasses system design using the waterfall model, including stages of requirement analysis, system design, implementation, integration, and testing. Smart contracts are utilized to automate certificate verification processes, enhancing efficiency and reliability. The research findings demonstrate that the blockchain-based system not only secures the certification process but also streamlines operations by eliminating intermediaries. This innovative approach holds promise for broader applications in academic institutions and beyond, offering a scalable solution for secure document management. Future studies may expand its applicability across diverse industries.
Decentralized finance (DeFi) technologies, when integrated with enterprise systems, create an opportunity to fully automate payment processes within ERP systems. This research develops and tests a smart contract-based cryptocurrency payment gateway framework with SAP ERP systems focusing on the FI, MM, and SD modules. Incorporating blockchain wallets, programmable transaction logic, and modular APIs enables SAP systems to independently manage multi-chain and multi-currency crypto payment initiation, confirmation, and reconciliation across multiple chains and currencies. The payment gateway minimizes payment delay, manual processing, and expenses in comparison to traditional fiat gateways utilizing smart contracts for payment validation, tokenization, and invoice reconciliation. Testing under real SAP transactional simulations on Quorum-based testnets showed an about 62% increase in reconciliation speed, 48% decrease in average cost per transaction, and 92% accuracy in volatile token conflict detection—during token volatility periods—resulting in errors. This study contributes to the development of frameworks for decentralized payment systems within ERP infrastructures, advancing the design and ERP enterprises aimed at achieving seamless interoperability, auditability, and comprehensive control over digital assets with SAP ecosystems.
This research provides a detailed analysis of multi-factor authentication (MFA) in Zero-Trust Architecture (ZTA).It focused the discussion on current practices and critical challenges encountered, sharing some insights into the future direction by finding "gaps.""The field of Cyber security is a constantly changing environment.From the beginning of "trust but verify," it has gradually changed to "always verify, never trust."In this case, MFA becomes a key and effective measure to enhance confidentiality in ZTA.ZTA requires that all entities within the system must verify their identities on an ongoing basis, often using MFA.With the widespread use of telecommuting, cloud services, and the Internet of Things, the demand for identity authentication is also increasing.The MFA uses multiple authentication steps to enhance security and trust in the system.However, implementing and applying MFA in the ZTA environment has not been smooth sailing.Some schemes directly affect the popularity of MFA in their implementation, such as poor user experience, complex integration, and poor scalability.The author first reviewed some of the existing MFA programs to get to the root cause and try to fix the problem.By analyzing these typical cases, best practices are found, and strategies for improvement are proposed.The aim is to promote a balance between ease of use and security in MFA.Finally, through literature review and case studies, as well as the exploration of emerging technologies such as adaptive MFA and zero-knowledge proof, The author explore some new approaches to improve the ease and efficiency of MFA in ZTA systems.
The rapid expansion of remote work has prompted organizations to seek cloud-based solutions that ensure data integrity, robust security, compliance with industry regulations, and optimal performance. Blockchain technology, recognized for its immutable distributed ledger capabilities, presents a compelling value proposition for securing remote workforce management platforms. This paper examines the integration of blockchain technology into cloud-based remote workforce management infrastructures, with a focus on data security, regulatory compliance, and performance optimization. Using hypothetical but realistic enterprise scenarios, real-time data metrics, and visual aids, we demonstrate how blockchain-backed cloud solutions can mitigate security breaches, streamline compliance reporting, and improve operational efficiency. The study concludes tshat blockchain’s decentralized verification mechanisms, combined with the scalability and reliability of modern cloud platforms, can significantly enhance the management of distributed teams while satisfying the evolving demands of data governance.
Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón · 5 authors
This paper evaluates the technical feasibility of blockchain technology within the healthcare ecosystem, with a focus on the use of Corda Distributed Ledger Technology (DLT) to ensure data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity—ensuring that transmitted data remains unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda Network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda blockchain technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.
Dongliang Cai, Liang Zhang, Borui Chen, Haibin Kan
Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization. Users generate their own public and secret keys and then register their keys and attributes with a transparent key curator. However, RABE still suffers from heavy decryption overhead. A natural approach to address this issue is to outsource decryption to a decryption cloud server (DCS). In this work, we propose the first auditable RABE scheme with reliable outsourced decryption (ORABE) based on blockchain. First, we achieve verifiability of transform ciphertext via a verifiable tag mechanism. Then, the exemptibility, which ensures that the DCS escapes false accusations, is guaranteed by zero knowledge fraud proof under the optimistic assumption. Additionally, our system achieves fairness and auditability to protect the interests of all parties through blockchain. Finally, we give concrete security and theoretical analysis and evaluate our scheme on Ethereum to demonstrate feasibility and efficiency.
The integration of blockchain technology into automated incident management systems represents a significant advancement in securing and validating system logs and incident records. This article presents a comprehensive article analysis of blockchain's application in incident management, examining its role in creating immutable audit trails and enhancing security controls. Through systematic review of implementation patterns and industry case studies, the article explores how distributed ledger technology addresses traditional challenges in log integrity and incident response validation. The article investigates the architectural frameworks necessary for successful blockchain integration, including considerations for scalability, performance, and regulatory compliance. The findings demonstrate that blockchain-based incident management systems offer enhanced transparency, improved audit capabilities, and robust security measures compared to traditional approaches. Additionally, the article examines emerging patterns in enterprise adoption, implementation challenges, and the synergies between blockchain and other emerging technologies in the incident management landscape. This article contributes to the growing body of knowledge on blockchain applications in enterprise security operations and provides a framework for organizations considering blockchain adoption for their incident management processes. The article concludes with recommendations for implementation and identifies areas for future research in this rapidly evolving field.
Cai, Dongliang, Borui Chen, Liang Zhang, Haibin Kan
Attribute-based encryption (ABE) is a generalization of public-key encryption that enables fine-grained access control in cloud services. Recently, Hohenberger et al. (Eurocrypt 2023) introduced the notion of registered ABE, which is an ABE scheme without a trusted central authority. Instead, users generate their own public/secret keys and then register their keys and attributes with a key curator. The key curator is a transparent and untrusted entity and its behavior needs to be audited for malicious registration. In addition, pairing-based registered ABE still suffers the heavy decryption overhead like ABE. A general approach to address this issue is to outsource decryption to a decryption cloud service (DCS).In this work, we propose BA-ORABE, the first fully auditable registered ABE with reliable outsourced decryption scheme based on blockchain. First, we utilize a verifiable tag mechanism to achieve verifiability of ciphertext transformation, and the exemptibility which enables the honest DCS to escape from wrong claims is guaranteed by zero knowledge fraud proof under optimistic assumption. Additionally, our system achieves fairness and decentralized outsourcing to protect the interests of all parties and the registration and outsourcing process are transparent and fully auditable through blockchain. Finally, we give security analysis, implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, and show its advantages in real application of decentralized finance.
In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.
Blockchain technology has emerged as a transformative tool for enhancing security, transparency, and scalability in distributed learning systems. This paper explores how blockchain can address critical challenges in these systems, such as data integrity, trust among participants, and scalability of learning models. By leveraging blockchain's decentralized ledger and smart contract capabilities, it becomes possible to establish secure data sharing, mitigate privacy concerns, and promote collaborative learning without reliance on central authorities. Furthermore, we discuss novel consensus mechanisms optimized for distributed learning, ensuring efficiency and scalability. Real-world applications in sectors like healthcare, finance, and education are also reviewed to highlight the practical benefits of integrating blockchain into distributed learning systems. Finally, the paper identifies open research challenges and future directions to advance this interdisciplinary field.
Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to provide confidentiality and fine-grained access control. Nonetheless, authority accountability and key abuse are critical issues that practical applications must address. Few studies have considered CP-ABE key confidentiality and authority accountability simultaneously. To our knowledge, we are the first to fill this gap by integrating non-interactive zero-knowledge (NIZK) proofs into CP-ABE keys and outsourcing the verification process to a smart contract. To meet the decentralization requirement, we incorporate a decentralized CP-ABE scheme into the proposed data sharing system. Additionally, we provide an implementation based on smart contract to determine whether an access control policy is satisfied by a set of CP-ABE keys. We also introduce an open incentive mechanism to encourage honest participation in data sharing. Hence, the key abuse issue is resolved through the NIZK proof and the incentive mechanism. We provide a theoretical analysis and conduct comprehensive experiments to demonstrate the feasibility and efficiency of the data sharing system. Based on the proposed accountable approach, we further illustrate an application in GameFi, where players can play to earn or contribute to an accountable DAO, fostering a thriving metaverse ecosystem.
The burgeoning size and complexity of Large Language Models (LLMs) introduce significant challenges in ensuring data integrity. The proliferation of "deep fakes" and manipulated information raises concerns about the vulnerability of LLMs to misinformation. Traditional LLM architectures often lack robust mechanisms for tracking the origin and history of training data. This opaqueness can leave LLMs susceptible to manipulation by malicious actors who inject biased or inaccurate data. This research proposes a novel approach integrating Blockchain Technology (BCT) within the LLM data supply chain. With its core principle of a distributed and immutable ledger, BCT offers a compelling solution to address this challenge. By storing the LLM's data supply chain on a blockchain, we establish a verifiable record of data provenance. This allows for tracing the origin of each data point used to train the LLM, fostering greater transparency and trust in the model's outputs. This decentralised approach minimises the risk of single points of failure and manipulation. Additionally, the immutability of blockchain records ensures that the data provenance remains tamper-proof, further enhancing the trustworthiness of the LLM. Our approach leverages three critical features of BCT to strengthen LLM security: 1) Transaction Anonymity: While data provenance is recorded on the blockchain, identities of data contributors can be anonymised, protecting their privacy while ensuring data integrity. 2) Decentralised Repository: Enhances the system's resilience against potential attacks by distributing the data provenance record across the blockchain network. 3) Block Validation: Rigorous consensus mechanisms ensure the validity of each data point added to the LLM's data supply chain - minimising the risk of incorporating inaccurate or manipulated data into the training process. Using the experimental approach, initial evaluations using simulated LLM training data on a blockchain platform demonstrate the feasibility and effectiveness of the proposed approach in enhancing data integrity. This approach has far-reaching implications for ensuring the trustworthiness of LLMs in various applications.
Covid19 pandemic has affected many sectors including education. All types of schools (public and private) have started to provide online education systems to their students to prevent spreading the disease. The online education system has brought many advantages besides stopping the spread of the disease. The students have more time since the lectures are not in class, it reduces the cost for the students since they physically do not go to school, and it provides flexibility in the lectures that students decide their schedule to attend the classes. However, there are some challenges in the online education system. The system needs to be available for the students when they take exams, view their exam results, and upload their exams for assessment. Since the lectures and exams are taken online, the data is very crucial. The data contains sensitive information such as exams, answers, scores, name, date of birth, address, phone number, government identification number, etc. A traditional online education system has a centralized infrastructure governed and managed by a single entity. This results in the system having single-point-of-failure attacks. This paper proposes an online examination system based on a smart contract and a blockchain. The blockchain eliminates single-point-of-failure attacks. The teachers write questions and store them in the blockchain. Only authorized students retrieve the exams from the blockchain by using smart contracts. The students submit their completed exams at predefined times. Then, the teachers evaluate the students’ exams and put the results into the blockchain. Their exam scores are protected from any unauthorized entities by encryption. Students can freely see and view their scores at any time. Then, the students can show their results to any other third parties (once they apply for an internship or job) that they have completed the courses successfully. The system also uses a decentralized storage (off-chain) system to eliminate scalability problems. Off-chain storage (InterPlanetary File System) stores students’ exams, answers, and exam results while the corresponding content identifiers of the files are stored in the blockchain. The proposed system is resilient to malicious teachers who can manipulate the exam results. In addition, the proposed system also provides a method for dishonest students who can complain about their exam results. In other words, the proposed system solves any conflicts between entities.
Ertem Nusret Tas, István András Seres, Yinuo Zhang, Márk Melczer · 7 authors
We introduce a blockchain Fair Data Exchange (FDE) protocol, enabling a storage server to transfer a data file to a client atomically: the client receives the file if and only if the server receives an agreed-upon payment. We put forth a new definition for a cryptographic scheme that we name verifiable encryption under committed key (VECK), and we propose two instantiations for this scheme. Our protocol relies on a blockchain to enforce the atomicity of the exchange and uses VECK to ensure that the client receives the correct data (matching an agreed-upon commitment) before releasing the payment for the decrypting key. Our protocol is trust-minimized and requires only constant-sized on-chain communication, concretely 3 signatures, 1 verification key, and 1 secret key, with most of the data stored and communicated off-chain. It also supports exchanging only a subset of the data, can amortize the server's work across multiple clients, and offers a general framework to design alternative FDE protocols using different commitment schemes. A prominent application of our protocol is the Danksharding data availability scheme on Ethereum, which commits to data via KZG polynomial commitments. We also provide an open-source implementation for our protocol with both instantiations for VECK, demonstrating our protocol's efficiency and practicality on Ethereum.
Michael L. Rosenberg, Tushar Mopuri, Hossein Hafezi, Ian Miers · 5 authors
Zero-knowledge Succinct Non-interactive ARguments of Knowledge (zkSNARKs) allow a prover to convince a verifier of the correct execution of a large computation in private and easily-verifiable manner.These properties make zkSNARKs a powerful tool for adding accountability, scalability, and privacy to numerous systems such as blockchains and verifiable key directories.Unfortunately, existing zkSNARKs are unable to scale to large computations due to time and space complexity requirements for the prover algorithm.As a result, they cannot handle real-world instances of the aforementioned applications.In this work, we introduce Hekaton, a zkSNARK that overcomes these barriers and can efficiently handle arbitrarily large computations.We construct Hekaton via a new "distribute-and-aggregate" framework that breaks up large computations into small chunks, proves these chunks in parallel in a distributed system, and then aggregates the resulting chunk proofs into a single succinct proof.Underlying this framework is a new technique for efficiently handling data that is shared between chunks that we believe could be of independent interest.We implement a distributed prover for Hekaton, and evaluate its performance on a compute cluster.Our experiments show that Hekaton achieves strong horizontal scalability (proving time decreases linearly as we increase the number of nodes in the cluster), and is able to prove large computations quickly: it can prove computations of size 2 35 gates in under an hour, which is much faster than prior work.Finally, we also apply Hekaton to two applications of realworld interest: proofs of batched insertion for a verifiable key directory and proving correctness of RAM computations.In both cases, Hekaton is able to scale to handle realistic workloads with better efficiency than prior work.
Interactive theorem provers (ITPs), such as Lean and Coq, can express formal proofs for a large category of theorems, from abstract math to software correctness. Consider Alice who has a Lean proof for some public statement T. Alice wants to convince the world that she has such a proof, without revealing the actual proof. Perhaps the proof shows that a secret program is correct or safe, but the proof itself might leak information about the program's source code. A natural way for Alice to proceed is to construct a succinct, zero-knowledge, non-interactive argument of knowledge (zkSNARK) to prove that she has a Lean proof for the statement T.
In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.
Blockchain technology has emerged as a transformative solution in the realm of cybersecurity, addressing critical challenges of data integrity and transparency.The ever-increasing sophistication of cyber threats necessitates robust mechanisms to secure sensitive data and ensure accountability in digital systems.Traditional methods, while effective to some extent, often fail to prevent data tampering and lack comprehensive traceability, leaving organizations vulnerable to breaches.Blockchain's decentralized, immutable ledger offers an innovative approach to overcoming these limitations by ensuring secure data integrity and creating transparent audit trails.This paper explores the application of blockchain technology in enhancing cybersecurity frameworks, emphasizing its role in preventing unauthorized data modification and enabling traceability.By employing cryptographic hashing and consensus mechanisms, blockchain ensures data authenticity while eliminating single points of failure.Its capabilities are particularly relevant for industries with stringent regulatory requirements, such as finance, healthcare, and supply chain management, where data accuracy and accountability are paramount.Moreover, we investigate advanced blockchain models, including private and consortium blockchains, to balance scalability, efficiency, and confidentiality.Integration with complementary technologies like smart contracts and artificial intelligence further extends its utility, enabling automated security protocols and anomaly detection.Despite its promise, blockchain adoption faces challenges, including high energy consumption, scalability issues, and the need for standardization.This study provides a comprehensive analysis of blockchain's potential and limitations in cybersecurity, proposing future directions to optimize its effectiveness.By bridging gaps in technology and implementation, blockchain holds the potential to redefine secure digital interactions, ensuring trust and resilience in increasingly interconnected systems.
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23 % to 12.91 %.
This work presents the DGChain (Data-Git- for Blockchain) project. This Python package allows version control of data in blockchain and IPFS based on a DAO (decentralized autonomous organization) for managing data in the development cycles of reproducible computational scientific research. Analyzes the benefits of using this Blockchain-Based Decentralized Architecture to mediate collaborative interactions between developers compared to existing solutions. Presents a use case in developing a medical research project and typical IRIS example to offer the traceability of changes and provenance of metadata, data, and code in Data / Software Version Control systems through management of intrinsic hash-based persistent, immutable CIDs (Content Identifier) recorded in Merkle trees in the development cycle of its main products, publication, software source code, and Data to guarantee reproducibility and trustworthiness in computational scientific research using DGChain.
This paper explores the integration of blockchain technology to enhance data integrity in cloud computing environments. As data breaches and unauthorized access continue to challenge traditional cloud security measures, blockchain offers a decentralized solution that ensures tamper-proof record-keeping and accountability. By leveraging cryptographic techniques and distributed ledger technology, the proposed framework enables secure data storage, sharing, and validation processes. This study highlights key use cases, potential challenges, and the overall impact of blockchain on improving trust and reliability in cloud computing, paving the way for more robust data integrity solutions in various applications across industries.
Avraam Tepelidis, Eirini E. Mitsopoulou, Athanasios T. Patenidis, Kristina Livitckaia · 6 authors
Blockchain technology advancements have made it feasible to build secure, decentralized networks with numerous use cases in various industries, including banking, education, government, and health. In higher education, there is a significant need for certification and credential verification through digital means, as fake certificates are common in the labor market. The BlockAdemiC system presented in this article falls into solutions for such issues. BlockAdemiC is a blockchain-based educational platform that makes use of cutting-edge features in blockchain technology to ensure the required degree of trust both at the individual user and institutional level. Specifically, BlockAdemiC represents a digital distributed security system for the certification and verification of educational activities, degrees, and skills in higher education and lifelong learning, producing an immutable educational passport. Information distributed using blockchain enhances trust in the exchange of information between institutions, agencies, companies, and alumni and introduces a trustworthy mechanism for content verification and authentication. For self-sovereign identity and identification management, the system utilizes distributed ledger technology via Hyperledger frameworks and offers users authority over their identities and credentials, removing the need for the involvement of central authorities to validate and verify user identities. Smart contracts are additionally supported, which provide the connection for storing student activities that take place on educational platforms on the EOSIO blockchain. The BlockAdemiC was evaluated via a technological feasibility study focusing on the system’s performance. The initial findings show that the system can efficiently process a high load of queries in parallel similar to the real-world situation and can be further considered for large-scale deployment and use.
Zain Ul Islam Adil, Majid Iqbal Khan, Kahkishan Sanam, Saif Ur Rehman Malik · 6 authors
ABSTRACT Counterfeit medical devices pose a threat to patient safety, necessitating a secure device authentication system for medical applications. Resource‐constrained sensory nodes are vulnerable to hacking, prompting the need for robust security measures. Token‐based authentication schemes, such as one‐time passwords (OTPs), smart cards, key fobs, and mobile authentication apps, along with certificate‐based authentication methods, such as client and code‐signing, employ cryptographic frameworks like elliptical curve cryptography (ECC) and physical unclonable functions (PUF). However, these methods face challenges, including block sequence issues and susceptibility to side‐channel attacks. To address these issues, we propose a framework for mutual authentication using private Ethereum. This framework integrates private Ethereum and cryptographic techniques for encrypting and decrypting data using mathematical algorithms to overcome block sequence issues and side‐channel attacks. Similarly, fog nodes are utilised to enhance local computing, storage, and networking capabilities for sensors. The framework is evaluated using metrics such as communication costs, execution costs, and computation costs based on Ethereum gas consumption. The performance of the LightAuth framework is compared with that of the Smart Contracts Against Counterfeit IoMT (SCACIoMT) framework, designed for Internet of Medical Things (IoMT) devices. The effectiveness of LightAuth is verified through formal security analysis using BAN logic.