Tarik Chafiq, Rida Azmi, Abdelhamid Fadil, Mohammed Ouadoud
Blockchain technology holds transformative potential for geospatial data sharing by offering enhanced security, transparency, and decentralization. This paper explores the integration of blockchain into managing geospatial data, focusing on its capabilities to revolutionize data storage, identification, property rights confirmation, and traceability. Through a comprehensive review of current blockchain applications and a detailed analysis of scalability, privacy, security, and cost concerns, this study identifies key challenges hindering blockchain adoption in geospatial data workflows. To address these issues, the research proposes solutions such as off-chain scaling, advanced encryption techniques, and cloud-based blockchain infrastructures. The study emphasizes the importance of aligning blockchain applications with existing legal and regulatory frameworks, including GDPR and other data protection laws. While blockchain presents significant opportunities for improving geospatial data management, this study concludes that successful implementation requires overcoming technical and regulatory hurdles. Future research should focus on developing standardized protocols and exploring innovative use cases to maximize the benefits of blockchain in geospatial contexts. • Blockchain offers potential solution for geospatial data sharing. • Identifies and discusses the significant applications of blockchain for sharing geospatial data. • Blockchain can play a decisive role in handling deception in the field of geospatial data. • Blockchain technology offers secure & transparent geospatial data sharing. • Identify and ensure the integrity and reliability of the data.
The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.
Noor Ul Ain Tahir, Umer Rashid, Hassan Jalil Hadi, Naveed Ahmad · 7 authors
This study investigated the potential of blockchain technology to transform Electronic Health Record (EHR) administration, integrity, and security. EHRs store vital health information such as medical history, diagnosis, prescriptions, and imaging findings, which may be shared with healthcare professionals to improve patient care. The existing EHR systems have a centralized framework. These centralized systems have a single point of failure, data management, integrity, and security concerns. Blockchain technology provides a solution to these problems by delivering benefits such as safety, privacy, secrecy, and decentralization. This study presents a framework for adopting blockchain technology in EHR systems, providing a comprehensive, modular, and straightforward approach. Our proposed framework addresses the constraints of existing EHR systems by providing a platform for connected and interoperable EHRs. The proposed blockchain-based patient health records management framework demonstrates the potential to address the limitations of current centralized health records systems. It offers benefits such as data privacy and security, interoperability, audibility, decentralization, and automation through the use of smart contracts. The proposed framework is implemented in Ethereum. The evaluation, i.e., cost and performance results, show that this solution is reasonable and may be used on any blockchain network, whether it is permissioned or permissionless.
Many blockchain networks aim to preserve the anonymity of validators in the peer-to-peer (P2P) network, ensuring that no adversary can link a validator's identifier to the IP address of a peer due to associated privacy and security concerns. This work demonstrates that the Ethereum P2P network does not offer this anonymity. We present a methodology that enables any node in the network to identify validators hosted on connected peers and empirically verify the feasibility of our proposed method. Using data collected from four nodes over three days, we locate more than 15% of Ethereum validators in the P2P network. The insights gained from our deanonymization technique provide valuable information on the distribution of validators across peers, their geographic locations, and hosting organizations. We further discuss the implications and risks associated with the lack of anonymity in the P2P network and propose methods to help validators protect their privacy. The Ethereum Foundation has awarded us a bug bounty, acknowledging the impact of our results.
Research on Bitcoin (BTC) transactions is a matter of interest for both economic and network science fields. Although this cryptocurrency is based on a decentralized system, making transaction details freely accessible, making raw blockchain data analyzable is not straightforward due to the Bitcoin protocol specificity and data richness. To address the need for an accessible dataset, we present ORBITAAL, the first comprehensive dataset based on temporal graph formalism. The dataset covers all Bitcoin transactions from January 2009 to January 2021. ORBITAAL provides temporal graph representations of entity-entity transaction networks, snapshots and stream graph. Each transaction value is given in Bitcoin and US dollar regarding daily-based conversion rate. This dataset also provides details on entities such as their global BTC balance and associated public addresses.
This paper presents an in-depth exploration of Data Availability Sampling (DAS) and sharding mechanisms within decentralized systems through simulation-based analysis. DAS, a pivotal concept in blockchain technology and decentralized networks, is thoroughly examined to unravel its intricacies and assess its impact on system performance. Through the development of a simulator tailored explicitly for DAS, we embark on a comprehensive investigation into the parameters that influence system behavior and efficiency. A series of experiments are conducted within the simulated environment to validate theoretical formulations and dissect the interplay of DAS parameters. This includes an exploration of approaches such as custody by row, variations in validators per node, and malicious nodes. The outcomes of these experiments furnish insights into the efficacy of DAS protocols and pave the way for the formulation of optimization strategies geared towards enhancing decentralized network performance. Moreover, the findings serve as guidelines for future research endeavors, offering a nuanced understanding of the complexities inherent in decentralized systems. This study not only contributes to the theoretical understanding of DAS but also offers practical implications for the design, implementation, and optimization of decentralized systems.
The rapid dynamics of cryptocurrency markets and the specific convolution of blockchain technology involve both challenges and opportunities of implementing Large Language Models in this area. In the present research, we consider the process of fine-tuning and applying LLMs in the cryptocurrency sector to meet its specific needs. Through the comprehensive analysis of the dataset rationale and model’s preparation, as well as multiple practical implications in cryptocurrency workflows, it is possible to demonstrate that LLMs significantly contribute to cryptocurrency analytics, fraud identification, smart contract processing, and customer interaction potential. The paper also addresses the issues of the cryptocurrency sector, such as security, privacy, and regulation, and proposes recommendations for further research and practical implementation.
Supply chain data management faces challenges in traceability, transparency, and trust. These issues stem from data silos and communication barriers. This research introduces DID-Chain, a framework leveraging blockchain technology, Decentralized Identifiers, and the InterPlanetary File System. DIDChain improves supply chain data management. To address privacy concerns, DIDChain employs a hybrid blockchain architecture that combines public blockchain transparency with the control of private systems. Our hybrid approach preserves the authenticity and reliability of supply chain events. It also respects the data privacy requirements of the participants in the supply chain. Central to DIDChain is the cheqd infrastructure. The cheqd infrastructure enables digital tracing of asset events, such as an asset moving from the milk-producing dairy farm to the cheese manufacturer. In this research, assets are raw materials and products. The cheqd infrastructure ensures the traceability and reliability of assets in the management of supply chain data. Our contribution to blockchain-enabled supply chain systems demonstrates the robustness of DIDChain. Integrating blockchain technology through DIDChain offers a solution to data silos and communication barriers. With DIDChain, we propose a framework to transform the supply chain infrastructure across industries.
In this paper, we undertake a thorough comparative examination of data resources pertinent to Non-Fungible Tokens (NFTs) within the framework of Machine Learning (ML). The core research question of the present work is how the integration of ML techniques and NFTs manifests across various domains. Our primary contribution lies in proposing a structured perspective for this analysis, encompassing a comprehensive array of criteria that collectively span the entire spectrum of NFT-related data. To demonstrate the application of the proposed perspective, we systematically survey a selection of indicative research works, drawing insights from diverse sources. By evaluating these data resources against established criteria, we aim to provide a nuanced understanding of their respective strengths, limitations, and potential applications within the intersection of NFTs and ML.
Nowadays, Internet of Things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. The challenge is to develop a solution for data exchange while proving and verifying the authenticity of the data from end-to-end. In line with this objective, this paper proposes a novel solution with the proper protocols to provide Trust in Data, making use of two Roots of Trust that are the IOTA Distributed Ledger Technology and the Trusted Platform Module. The paper presents the design of the proposed solution and discusses the key design aspects and relevant trade-offs. The paper concludes with a Proof-of-Concept implementation and an experimental evaluation to confirm its feasibility and to assess the achievable performance.
Battula Venkata Satish Babu, Kare Suresh Babu, Durga Prasad Kare
The secure access and reliable access revocation methods of modern digital systems are based on access control mechanisms.Access policies, which are used in access control mechanisms, are very important in safeguarding security and ensuring data protection.It is evident that the protection and tamper-proofing of such policies are very important.In addition, efficient access revocation schemes are required to promptly remove access privileges when users are no longer needed or authorized.The shortcomings of existing systems in ensuring efficient, streamlined access revocation and tamper-proof protection of access control policies underscore the need for innovative solutions.In this paper, we have introduced the novel Blockchain Attribute-Based Secure Data Management Model (BAB-SDMM).Our model is the first to integrate attribute-based encryption (ABE), Attribute-Based Access Control (ABAC), and blockchain to achieve multiple security features as well as provide partial and complete revocation at the same time.The experimental results and analysis, performed using the Ethereum blockchain network, demonstrated the enhanced performance of the proposed BAB-SDMM compared to existing research works.
Principal Engineer, Discover Financial Services, Houston, TX, USA, AdisheshuReddy Kommera
The Event-Driven Data Mesh Integration pattern revolutionizes modern data sharing by blending eventdriven architecture and the data mesh paradigm. This innovative approach decentralizes data ownership, enabling organizational domains to manage their data autonomously while ensuring real-time responsiveness and seamless scalability. Central to the pattern are event broker layers, domain-oriented data producers, event enrichment nodes, and a self-serve data product catalog. Key features include schema validation, policy-based governance, and real-time enrichment, fostering efficiency, compliance, and agility. Integrating AI-powered self-healing mechanisms further enhances resilience, automates recovery processes, and optimizes resource allocation. Applications span various sectors, from operational systems to analytics pipelines, enabling real-time decision-making and continuous improvement. This approach empowers organizations to innovate faster while maintaining robust data governance, scalability, and interoperability across autonomous domains, paving the way for intelligent and dynamic data ecosystems.
Jan 1, 2024·Proceedings of the 4th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 2024): "Creating solutions for a sustainable future: technology-based entrepreneurship"
Blockchain emerges as an innovative technology with potential applications in the healthcare sector due to its demonstrated qualities of decentralization, distribution, and data integrity. This systematic literature review (SLR), without metaanalysis, aims to analyze current perspectives and trends in blockchain interoperability for information systems in the healthcare sector, focusing on challenges and opportunities to enhance healthcare data management through this technology. Using the PICO strategy and PRISMA methodology, 25 openaccess articles from Scopus and PubMed databases were reviewed, addressing blockchain interoperability and healthcare between 2020 and 2024. The results highlight those perspectives on blockchain interoperability in healthcare focus on improving efficiency and security in data exchange through a decentralized network. Furthermore, trends indicate the use of platforms and standards such as FHIR, IPFS, Ethereum, and Hyperledger to facilitate this exchange. In conclusion, this study underscores blockchain's potential to transform health data management and exchange through cryptographic mechanisms that enhance the security and efficiency of information systems. It also identifies trends and the use of these platforms and standards that contribute to achieving interoperability.
Owen Chaffard, Pablo Mollá, Marc Cavazza, Helmut Prendinger
In the recent advancements in application of deep learning to time series forecasting, focus has shifted from training transformers end-to-end to efficiently leveraging the predictive capabilities of Large Language Models (LLMs). Models that encode the time series data to interact with a frozen LLM backbone have been shown to outperform transformers on all benchmark datasets. However, their efficiency on complex datasets, which do not show clear seasonality or trend, remains an open question. In this work, we seek to evaluate the performance of reprogrammed LLMs on the Bitcoin price chart, a financial time series known for its complexity and high volatility. We propose effective methods to improve the performance of Time-LLM, a State-of-the-art (SOTA) method, on such a time series. First, we propose structural improvements to Time-LLM. Second, we suggest an efficient way to handle the non-stationarity of the dataset. Finally, we propose an efficient method for passing additional financial information to the LLM. Our results demonstrate a 50% improvement on the average percentage loss and a 5% increase on accuracy of our adapted Time-LLM architecture on Bitcoin data when compared to SOTA models, including the original Time-LLM model. This highlights the impact on forecast accuracy of domain-specific decision making in data processing and feature selection.
Davide Frey, Lucie Guillou, Michel Raynal, François Taı̈ani
This paper explores the territory that lies between best-effort Byzantine-Fault-Tolerant Conflict-free Replicated Data Types (BFT CRDTs) and totally ordered distributed ledgers, such as those implemented by Blockchains. It formally characterizes a novel class of distributed objects that only requires a First In First Out (FIFO) order on the object operations from each process (taken individually). The formalization leverages Mazurkiewicz traces to define legal sequences of operations and ensure both Strong Eventual Consistency (SEC) and Pipleline Consistency (PC). The paper presents a generic algorithm that implements this novel class of distributed objects both in a crash- and Byzantine setting. It also illustrates the practical interest of the proposed approach using four instances of this class of objects, namely money transfer, Petri nets, multi-sets, and concurrent work stealing dequeues.
Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention, emerging as a new field with the potential to address the limitations of each. In this article, we will focus on the integration of web3 and the AI marketplace, where AI services and products can be provided in a decentralized manner (DeAI). A comprehensive review is provided by summarizing the opportunities and challenges on this topic. Additionally, we offer analyses and solutions to address these challenges. We've developed a framework that lets users pay with any kind of cryptocurrency to get AI services. Additionally, they can also enjoy AI services for free on our platform by simply locking up their assets temporarily in the protocol. This unique approach is a first in the industry. Before this, offering free AI services in the web3 community wasn't possible. Our solution opens up exciting opportunities for the AI marketplace in the web3 space to grow and be widely adopted.
Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records - off-chain storage retrieval with Merkle root verification and a brute-force search - the off-chain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic data.
Esa Fauzi, Sy Yuliani, Yenie Syukriyah, Azizah Zakiah
Single Sign-On (SSO) is a mechanism that allows users to access various services using a single set of login credentials. However, in SSO implementations, there are still challenges related to security and authentication management, particularly attacks targeting the Identity Provider (IDP). To address this, the use of Non-Fungible Tokens (NFTs) as proof of IDP ownership has been proposed as a solution to enhance security in the authentication mechanism. The utilization of NFTs in SSO with OpenID Connect and OAuth 2.0 has the potential to improve security and convenience in the authentication process due to the unique and non-duplicable nature of NFTs. The results of this research present a model and design of SSO with NFTs on OpenID Connect and OAuth 2.0. An SSO application with login, register, and password recovery features was also developed to provide convenience to users during the login process. The findings conclude that the utilization of NFTs in SSO with OpenID Connect and OAuth 2.0 has the potential to enhance security and convenience in the authentication mechanism. Further research is needed to explore aspects such as scalability, in-depth security analysis, testing in real-world scenarios, improvement of integration and interoperability, as well as comparative analysis with other SSO technologies.
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively. The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data exchanged between organizations that integrates Blockchain technologies with knowledge graphs. We detect substance use disorder data in patient encounters exchanged using clinical terminology based upon the value sets provided by the National Institutes of Health for the Substance Abuse and Mental Health Services Administration. Generally, we consider sharing Part 2 data without consent as Byzantine medical faults, as they represent data shared between known and trusted network participants, that is valid, but is not relevant, and sharing it causes a breach. In this paper, we present our methodology in detail along with the experiment results. We model a medical network of hospitals based upon the most recent healthcare legislation, TEFCA, and generate synthetic patient encounter data dynamically in HL7 format. We convert exchanged encounter data into a knowledge graph data model so that we can use SNOMED-CT for identifying Part 2 data. For cohorts of 1,000 patients, we detect Part 2 data in a subset of their encounter data shared between organizations and log that securely on an Ethereum-based blockchain.
Millions of individuals today utilize cryptocurrencies, which have a strong open-source community and payment network. The first study to predict cryptocurrency prices using news and social media emotion was published in In this paper, we apply sentiment analysis and machine learning principles to find the correlation between "public sentiment" and "market sentiment". We use twitter data to predict public mood and use the predicted mood and cryptocurrency financial news to predict the market movements.
In the development of large-scale, integrated systems, the consistent clarity and distribution of knowledge and relevant status is crucial to success. There are many existing methods and commercial tools for providing context and traceability from requirements and specifications to a system's artifacts, but these tools are often vendor-locked and require access to cloud-based services and necessitate high licensing costs. Further, modern large-scale systems development involves multiple business partners, each of which needs to ensure their teams have granular, role-based access to all relevant information without impediment; centralized warehousing and gatekeeping should not be handled by a single entity if the information is to remain readily accessible. Instead, a permissioned, distributed knowledgebase that avoids vendor lock-in enables a consistent, real-time view of information that provides equivalent context for developers and other stakeholders. ChaordicLedger, a free and open source (FOSS) project joins the transparency and smart contract aspects of Distributed Ledger Technology with the storage capabilities of a Distributed File System to fulfill this industrial application while allowing for industry-specific customizations.