Blockchain Security Research Articles

Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments

This paper examines the complexities of clinical trial contract negotiations, focusing on legal strategies and technological innovations that safeguard the interests of sponsors, vendors, and institutions. In an increasingly globalized and digital research environment, contract negotiations are pivotal to ensuring compliance, protecting intellectual property, and mitigating legal risks. The study’s purpose was to explore key components of clinical trial contracts, address the challenges involved, and assess future trends, particularly in light of evolving regulatory frameworks and technological advancements. Through a comprehensive review of legal literature and emerging technologies, the study identified crucial aspects of contract negotiations, including risk allocation, indemnification, intellectual property rights, and data privacy. Special attention was given to the role of technology—such as AI, blockchain, and contract management platforms—in streamlining the negotiation process. These tools have proven effective in reducing errors, enhancing collaboration, and ensuring regulatory compliance. The main findings revealed that while traditional contract structures remain integral, the adoption of digital tools and a patient-centric approach are critical in addressing new challenges posed by decentralized trials and cross-border collaborations. The study concluded that stakeholders must embrace flexible contract frameworks, incorporate robust data protection measures, and leverage technology to optimize negotiations. Recommendations include the integration of advanced analytics for decision-making, the adoption of blockchain for contract security, and a heightened focus on patient rights in contract structures to ensure ethical conduct and compliance in clinical research.

The Utilization of Blockchain for Data Security for the Chronic Pain Physician.

The COVID-19 pandemic accelerated the surge in medical data utilization, notably impacting chronic pain management given its enormous economist cost. While the collection and use of data enhances research and care quality, data exchange risks compromising integrity and privacy, exemplified by the Change Healthcare cyberattack. Here, we review the potential of blockchain for the utilization for cybersecurity in the healthcare system with an emphasis on the field of chronic pain. Blockchain technology emerges as a potential solution, offering decentralized, secure, and immutable data management. Blockchain ensures transparency, integrity, and data privacy, which is crucial in healthcare. Smart contracts may offer automated, secure management of implantable neuromodulation devices such as spinal cord stimulators and intrathecal pumps. Blockchain's potential in pharmaceutical supply chain integrity is exemplified in preliminary efforts ensuring that the medication retrieved by the patient is indeed the intended medication. Despite limitations such as speed of transactions, blockchain presents innovative avenues for healthcare security and quality improvement, necessitating further development for widespread implementation. Blockchain's applicability is not only applicable to chronic pain management, but can be used in medicine as a whole.

Towards Scalable and Secure Blockchain in Internet of Things: A Preference-Driven Committee Member Auction Consensus Approach

Blockchain technology is acclaimed for eliminating the need for a central authority while ensuring stability, security, and immutability. However, its integration into Internet of Things (IoT) environments is hampered by the limited computational resources of IoT devices. Consensus algorithms, vital for blockchain safety and efficiency, often require substantial computational power and face challenges related to security, scalability, and resource demands. To address these critical issues, we propose a novel model that significantly enhances the security and performance of blockchain in IoT environments. Our model introduces three key innovations: (1) a bidirectional-linked blockchain system that strengthens security against long-range attacks by exploiting dual reference points for block validation; (2) the integration of user preferences into the Committee Member Auction (CMA) consensus algorithm, optimizing miner selection to balance resource efficiency with security; and (3) a comprehensive performance and frequency analysis that demonstrates the system’s resilience against double-spend, long-range, and eclipse attacks. The proposed model not only reduces block validation delays but also enhances overall system performance, as evidenced by simulations comparing its effectiveness with existing CMA algorithms. These advancements have the potential to significantly impact the deployment of blockchain in resource-constrained IoT environments, offering a more secure and efficient solution.

Quantum-Resilient Self-Evolving Blockchains: AI- Driven Consensus and Autonomous Security Upgrades

The fragility of conventional blockchain systems is becoming more apparent in face of the relentless march forward in quantum computing. In this paper, we suggest the optimal architecture for quantum-resistant blockchains deploying AI regarding consensus self- evolving security protocols and mechanisms. An intuitive example of this integration is the use of machine learning algorithms for a self-adapting security system, which automatically adjusts the protection on its infrastructure capabilities to evolving quantum threats. In this paper, we address the consequences of quantum computing in blockchain security introduce a framework for AI-assisted consensus (Section 3) and cover the feasibility of self- evolving blockchains that guarantee their reliability as well as integrity in a post-quantum era.

A secure blockchain framework for healthcare records management systems

AbstractElectronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.

Technical sandbox for a Global Patient co-Owned Cloud (GPOC)

BackgroundThe use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research.MethodsWe constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development.ResultsHere, we present the GPOC series’ technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management.The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept.The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations.ConclusionsThe GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health.

Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm

The advancements in quantum computing and the potential for polynomial-time solutions to traditional public key cryptography (i.e., Rivest–Shamir–Adleman (RSA) and elliptic-curve cryptography (ECC)) using Shor’s algorithm pose a serious threat to the security of pre-quantum blockchain technologies. This paper proposes an efficient quantum-safe blockchain that incorporates new quantum-safe consensus algorithms. We integrate post-quantum signature schemes into the blockchain’s transaction signing and verification processes to enhance resistance against quantum attacks. Specifically, we employ the Falcon signature scheme, which was selected during the NIST post-quantum cryptography (PQC) standardization process. Although the integration of the post-quantum signature scheme results in a reduction in the blockchain’s transactions per second (TPSs), we introduce efficient approaches to mitigate this performance degradation. Our proposed post-quantum delegated proof of luck (PQ-DPoL) combines a proof of luck (PoL) mechanism with a delegated approach, ensuring quantum resistance, energy efficiency, and fairness in block generation. Experimental results demonstrate that while post-quantum cryptographic algorithms like Falcon introduce larger signature sizes and slower processing times, the PQ-DPoL algorithm effectively balances security and performance, providing a viable solution for secure blockchain operations in a post-quantum era.

Blockchain Security:"Botnets and Bitcoin Mining” - A Study on the Impacts and Countermeasures

Introduction: Botnets have become a significant threat to cybersecurity, as they can be used for a wide range of malicious activities, including Distributed Denial-of-Service (DDoS) attacks, spamming, and cryptocurrency mining. Bitcoin Mining, in particular, has become a lucrative target for cybercriminals, as it requires massive computing power and can generate significant profits. Method: In this paper, the author presents a study on a botnet that uses an HTA file to gain initial access and execute code on a victim's device, followed by the installation of mining software to infect the device and bitcoins. Result: The author analyzes the botnet's behaviour, including its evasion techniques and Bitcoin Mining activities, and discusses the implications of current findings for cybersecurity and Bitcoin Mining. Conclusion: Future research should also investigate the use of different command and control servers and other advanced attack frameworks in botnet operations and examine the potential connections between botnets and other cybercrime activities, such as ransomware and espionage.

A survey on blockchain security for electronic health record

A survey on blockchain security for electronic health record

Identifying patterns and mechanisms of AI integration in blockchain for e-voting network security

The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges

Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges

The rapid progression of quantum computing has created new opportunities for advancing blockchain technology, particularly in addressing its two primary challenges which are scalability and security. As blockchain adoption grows, traditional consensus mechanisms reveal significant inefficiencies, causing transaction bottlenecks and increased latency. Additionally, the advent of quantum computing poses a significant threat to the classical cryptographic algorithms foundational to blockchain security. This research introduces an innovative approach that harnesses quantum computing to address these dual challenges. To enhance scalability, this research proposes integrating Grover's algorithm and Post Quantum encryption alogrithms to optimize transaction selection and validation, significantly reducing time complexity compared to classical methods. On the security front, the research introduces post-quantum cryptographic techniques to strengthen blockchain networks against quantum adversaries. Simulations conducted on IBM Qiskit Quantum Computer Simulator demonstrate that the proposed framework achieves superior efficiency, cost-effectiveness, and faster transaction processing with Grover's algorithm and Post Quantum encryption alogrithms compared to the current classical systems. These improvements highlight the potential benefits of utilizing quantum computing to deploy quantum-enhanced blockchain. Keywords: Quantum, Computing, IBM, Qiskit, blockchain, Post-Quantum, Encryption CISDI Journal Reference Format Gabriel, O., Olabiyisi, S.O., Ismaila, W.O., Falade, O.A., & Alawode O. (2024): Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 3, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N3P2 Available online at www.isteams.net/cisdijournal

Retraction Note: Trusty URI learning-based secure blockchain management for making verifiable and reliable digital artifacts

Retraction Note: Trusty URI learning-based secure blockchain management for making verifiable and reliable digital artifacts

SentinelFusion based machine learning comprehensive approach for enhanced computer forensics.

In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering. Utilizing a comprehensive blockchain-based dataset of various criminal activities, the framework leverages multiple machine learning models, including support vector machines, K-nearest neighbors, naive Bayes, logistic regression, and decision trees, alongside the novel SentinelFusion ensemble model. Extensive evaluation metrics such as accuracy, precision, recall, and F1 score are used to assess model performance. The results demonstrate that SentinelFusion outperforms individual models, achieving an accuracy, precision, recall, and F1 score of 0.99. This study's findings underscore the potential of combining blockchain technology and machine learning to advance computer forensics, providing valuable insights for practitioners and researchers in the field.

Personalization and Customer Experience in E-Commerce

Innovation is crucial for maintaining a competitive edge and delivering exceptional shopping experiences in the ever-evolving realm of e-commerce. Businesses continuously seek creative approaches to enhance the e-commerce landscape, incorporating technologies like immersive experiences, seamless payment solutions, Artificial Intelligence (AI), and personalized services. This study investigates the emerging technologies and innovations within e- commerce, particularly focusing on the application and impact of AI. Utilizing conceptual research methodology and secondary data from various scholarly articles, journals, and online sources, the study concludes that recent innovations such as AI-driven personalization, Augmented Reality (AR) and Virtual Reality (VR), chatbots, blockchain for transaction security and supply chain management, social commerce, drone delivery, and e-wallet solutions have significantly advanced the e- commerce sector. Noteworthy AI applications include platforms like Google AI Cloud Platform, IBM Watson, BigML, and Infosys Nia, which are also leveraged for product pricing and distribution strategies. Additionally, the adoption of diverse payment methods such as e- wallets and UPI contributes to enhancing user satisfaction in e-commerce transactions.

Blockchain and Homomorphic Encryption for Data Security and Statistical Privacy

Blockchain and Homomorphic Encryption for Data Security and Statistical Privacy

Blockchain and ML in land registries a transformative alliance

This study presents a novel method for merging blockchain security and machine learning (ML) valuation to update land register systems. The system offers a safe, open, and effective framework for documenting and managing land ownership, addressing issues with conventional land registry procedures. Blockchain technology creates a tamper-proof record by cryptographically combining transactions and time-stamped entries to provide an immutable and decentralized ledger. In addition to building a solid foundation for the land registry system, this strengthens trust. Simultaneously, ML algorithms examine variables such as amenities and location to remove inflated pricing, providing accurate assessments and encouraging openness in the real estate sector. The system has been put into practice and verified in small-scale applications. Its features include enhanced data security, expedited ownership transfers, and accurate asset appraisals. Collaboration between governments, regulatory agencies, and technology suppliers is necessary for widespread deployment. Land registration procedures will change as a result of the revolutionary partnership between blockchain and ML technology, which offers a more effective, safe, and future-ready environment. Accepting this ground-breaking technique establishes a new benchmark for the updating of land ownership data and is a major step toward a more sophisticated and dependable method in the industry.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

Legal Aspects of Using Blockchain Technologies in the Field of E-Commerce on SDG'S

Objective: The objective of this study is legal aspects of blockchain. Theoretical Framework: Blockchain is one of the most prominent technologies and it has become a driving factor in business transformation. Method: Methods of historical and logical analysis and comparison, expert opinions, system analysis and forecasting were used. The research materials included legal aspects, in particular, in the development of blockchain technology. Results and Discussion: Blockchain, like any electronic technology, can have different forms of social implementation. Their openness is manifested in the fact that anyone can become a network node participating in the consensus procedure. Information in public blockchains is actually and legally publicly available. The listed qualities ensure a high level of security of public blockchains and a high level of users’ trust toward them. Research Implications: The legal regulation of digital technologies is becoming an important tool for ensuring a balance between public and private interests, benefits and risks of the digital age. Originality/Value: The global blockchain market will grow significantly in the coming years. The new vector of blockchain technology will be aimed at accelerating the pace of digitalization, financial inclusion and enhancing security. There is no doubt that blockchain is a disruptive technology and will continue to dominate the business world in the coming years.

Quantum calculi and formalisms for system and network security: A bibliographic insights and synoptic review

AbstractQuantum calculi and formalisms are useful tools for ensuring security and computational capabilities in blockchain and cryptography. They aid in designing and analysing new cryptographic protocols for blockchain, determining the behaviour of quantum operations in blockchain‐based smart contracts, assessing the feasibility and security of quantum algorithms in blockchain applications, and building a quantum‐safe blockchain system. A comprehensive review of the applications of quantum calculi and formalisms in computer security and network security, along with a bibliographic analysis is presented. It is unique in that it combines bibliometric analyses with a technical review of the domain of quantum calculi and formalism. Bibliometric and biographic analysis in the field helps identify research trends, assess the influence of research, determine collaboration patterns, evaluate journals, and examine publication behaviours, among other things. It performs bibliographic and bibliometric analysis using a dataset collected from Scopus and Web of Science through different queries. The obtained results help identify important institutions, authors, organisations, collaboration networks, keywords, and more. The provided open challenges and future vision pave the way for further research in the direction of quantum calculi and formalism applications in computer security and network security.

Integrating fully homomorphic encryption to enhance the security of blockchain applications

Blockchain has been widely used for secure transactions among untrusted parties, but the current design of blockchain does not provide sufficient privacy and security for the data on the chain, limiting its application in sensitive information scenarios. To address this problem, we propose integrating fully homomorphic encryption (FHE) to enhance the security of blockchain applications, which can extend the application scope of blockchain and improve the privacy and security of blockchain by the features of FHE. Our scheme classifies FHE into those supporting polynomial and non-polynomial operations and introduces the concept of ciphertext computation conversion into Ethereum, enabling conversion between different ciphertext computation types. Moreover, we analyse the security and correctness to explain the feasibility and availability of the scheme. We carry out comparative experiments using different open-source libraries for fully homomorphic encryption and the time performance evaluation of the ciphertext computation conversion under different thread counts. The experiment results demonstrate the efficiency and usability of our scheme.