Blockchain Papers

Raiane Coelho, Regina Braga, José David, Victor Ströele · 6 authors

No abstract is available for this record.

Shashank Joshi, Arhan Choudhury

A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the process of developing a knowledge market that is capable of enduring over time, and it is also an essential driver of the progression of scientific investigation. This paper underlines flaws associated with the conventional knowledge-based market, including but not limited to excessive financing concentration, ineffective information exchange, a lack of security, mapping of entities, etc. The authors present a decentralized framework for the knowledge marketplace incorporating technologies such as blockchain, active inference, zero-knowledge proof, etc. The proposed decentralized framework provides not only an efficient mapping mechanism to map entities in the marketplace but also a more secure and controlled way to share knowledge and services among various stakeholders.

Shuang Sun, Huayun Tang, Rong Du

Internet of Things (IoT) system provides personalized services to users by analyzing the amount of data collected from billions of sensors. It is important to ensure the trustworthiness of the heterogeneous IoT data. To address this requirement, the data provenance needs to be used in the IoT system. In this article, we integrate the PROV-DM model into the IoT architecture and propose an IoT data provenance model. To avoid corrupting and forging the provenance information, we record the provenance data onto the blockchain. In addition, we use the IoT secure and computable data sharing system to illustrate that the proposed model can construct a provenance graph to detect which agent operates the IoT data abnormally. Finally, we implement and evaluate the proposed model on a public IoT database.

Wenwen Ding, Jiachen Hou, Juanjuan Li, Chao Guo · 7 authors

Decentralized science (DeSci) is a hot topic emerging with the development of Web3 or Web3.0 and decentralized autonomous organizations (DAOs) and operations. DeSci fundamentally differs from the centralized science (CeSci) and Open Science (OS) movement built in the centralized way with centralized protocols. It changes the basic structure and legacy norms of current scientific systems via reshaping the cooperation mode, value system, and incentive mechanism. As such, it can provide a viable path for solving bottleneck problems in the development of science, such as oligarchy, silos, and so on, and make science more fair, free, responsible, and sensitive. However, DeSci itself still faces many challenges, including scaling, balancing the quality of participants, system suboptimal loops, lack of accountability mechanism, and so on. Taking these into consideration, this article presents a systematic introduction of DeSci, proposes a novel reference model with a six-layer architecture, addresses the potential applications, and also outlines the key research directions in this emerging field. This article is committed to providing helpful guidance and reference for future research efforts on DeSci.

Élton Carneiro Marinho, Éber Assis Schmitz, Sérgio Manuel Serra da Cruz

Blockchain technology combined with Data provenance is one way to make soil data more trustworthy and traceable by providing tamper-proof information about the origin, transformations, and history of pieces of data. We present Hyperledger Fabric of FAIRCHAIN, a computational infrastructure that manages smart contracts that uses soil data. We aim to mitigate the open challenges of the agricultural food supply chain, specifically in the difficulty of traceability of soil data. In this work, we present the mechanism to structure a smart contract using soil data enriched with retrospective provenance metadata. The infrastructure can hold workflow implementations.

Jakub Smékal, Bleu Knight, Brock McKean, Daniel Friedman

Transcript of: <strong>Active Inference ~ Twitter Spaces 002 ~ “Can Web3 survive without cognitive modeling?”</strong> Session 002.1, September 14, 2022 https://www.youtube.com/watch?v=Sb8A0jNzWPE

Luciano Baresi, Giovanni Quattrocchi, Damian A. Tamburri, Luca Terracciano

The deployment and management of Blockchain applications require non-trivial efforts given the unique characteristics of their infrastructure (i.e., immutability) and the complexity of the software systems being executed. The operation of Blockchain applications is still based on ad-hoc solutions that are error-prone, difficult to maintain and evolve, and do not manage their interactions with other infrastructures (e.g., a Cloud backend). This paper proposes KATENA, a framework for the deployment and management of Blockchain applications. In particular, it focuses on applications that are compatible with Ethereum, a popular general-purpose Blockchain technology. KATENA provides i) a metamodel for defining Blockchain applications, ii) a set of processes to automate the deployment and management of defined models, and iii) an implementation of the approach based on TOSCA, a standard language for Infrastructure-as-Code, and xOpera, a TOSCA-compatible orchestrator. To evaluate the approach, we applied KATENA to model and deploy three real-world Blockchain applications, and showed that our solution reduces the amount of code required for their operations up to $82.7\%$.

Nikos Kefalakis, Aikaterini Roukounaki, John Soldatos, Mauro Isaja

This chapter presents a dynamic and programmable distributed data analytics solution for industrial environments. The solution includes an edge analytics engine for analytics close to the field and in line with the edge computing paradigm. Each edge analytics engine instance is flexible and dynamically configurable based on an Analytics Manifest (AM). It is also based on distributed ledger technologies for configuring analytics tasks that span multiple edge nodes and instances of the edge analytics engine. In particular, it leverages ledger services for synchronizing and combining various AMs in factory wide analytics tasks. Based on these mechanisms, the presented distributed data analytics infrastructure is therefore flexible, configurable, dynamic and resilient. Moreover, it is open source and provides Open APIs (Application Programming Interfaces) that enable access to its functionalities. These features make it unique and valuable for vendors and integrators of industrial automation solutions.

Arghya Das

In this paper, we introduce SwarMED, a decentralized yet high throughput interoperability system for big biomedical data. SwarMED uses Etehreum blockchain for trustless security and Swarm p2p storage to handle high throughput transaction of big data. In SwarMED, we developed an indexing mechanism over the immutable storage of Swarm to achieve high-throughput while sharing millions of patient records and images among multiple parties. SwarMED achieved a high throughput of 250K medical records per second over a private network constructed over LSU-HPC cluster. This high throughput is 9x more comparing to conventional way of using p2p storage in conjunction with blockchain. This high throughput enables the patients to get realtime access to his comprehensive medical history and scientists to gain real-time access to different medical data for collaborative research complying to the constraints posed by existing laws. Our system-level analysis over different design alternatives over different transfer and storage architectures shows that, p2p storage platforms automatically provide significantly better scalability over traditional HTTP with increasing number of clients. Swarm provides 2x more I/O throughput and 10x less latency than IPFS, another p2p storage system making it a better choice for decentralized big data transaction.

Dragi Kimovski, Sasko Ristov, Radu Prodan

The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.

Haoxian Chen, Gerald Whitters, Mohammad Javad Amiri, Yuepeng Wang · 5 authors

This paper presents DeCon, a declarative programming language for implementing smart contracts and specifying contract-level properties. Driven by the observation that smart contract operations and contract-level properties can be naturally expressed as relational constraints, DeCon models each smart contract as a set of relational tables that store transaction records. This relational representation of smart contracts enables convenient specification of contract properties, facilitates run-time monitoring of potential property violations, and brings clarity to contract debugging via data provenance. Specifically, a DeCon program consists of a set of declarative rules and violation query rules over the relational representation, describing the smart contract implementation and contract-level properties, respectively. We have developed a tool that can compile DeCon programs into executable Solidity programs, with instrumentation for run-time property monitoring. Our case studies demonstrate that DeCon can implement realistic smart contracts such as ERC20 and ERC721 digital tokens. Our evaluation results reveal the marginal overhead of DeCon compared to the open-source reference implementation, incurring 14% median gas overhead for execution, and another 16% median gas overhead for run-time verification.

Markus Putnings

The article describes the use and possible value creation of Non-Fungible Tokens (NFT) in the academic and open access publishing environment. It defines NFTs, describes disadvantages and possible solutions, especially in the intended scientific environment. An overview of existing NFT service providers from the publishing environment illustrates that there is not yet a suitable one for researchers. Accordingly, three possible scenarios are shown where NFT services could be located in a science-friendly way. One would be with library- or scholarly-led university presses, repositories, and other publication infrastructures (such as OJS or OMP). Another would be to use centralizing and channelling article submission platforms with which universities have contracts, such asChronosHub. The third and broadest approach would be through Digital ObjectIdentifier (DOI) registration agencies such as ChronosHub and DataCite, although complexities come into play here due to the triangular relationship with publishers registering DOIs (some of them having exclusive usage rights transferred to themselves). This complexity could be reduced by registeringNFTs only for open access publications with a Creative Commons Attribution license. A summary and outlook provide an overview of open questions and initial starting points to get started.

Salvatore D’Antonio, Federica Uccello

With the advent of Industry 4.0, information has become a key aspect for virtually any system. Critical infrastructures haven't been excluded by this technological revolution, which has led to several advantages in terms of communication, interoperability, and scalability. On the other hand, these systems are now targeted by new attacks, previously exclusive to cybersystems. The potential of data violation ranges from the interruption of the service provided to, in the worst cases, disastrous consequences in environmental, economic and safety terms. Consequently, ensuring data reliability is an essential task to prevent these kinds of attacks. Data provenance, a kind of metadata that identifies the derivation history of a data, can provide a possible solution. This paper aims to discuss solutions for a tamper proof data provenance extended, but not limited, to the healthcare scenario. The proposed approach is based on blockchain technology to ensure protection of sensitive data, such as medical records and healthcare data.

Massimo Bartoletti, J. Chiang, Tommi Junttila, Alberto Lluch Lafuente · 6 authors

Decentralised Finance (DeFi) applications constitute an entire financial ecosystem deployed on blockchains. Such applications are based on complex protocols and incentive mechanisms whose financial safety is hard to determine. Besides, their adoption is rapidly growing, hence imperilling an increasingly higher amount of assets. Therefore, accurate formalisation and verification of DeFi applications is essential to assess their safety. We have developed a tool for the formal analysis of one of the most widespread DeFi applications: Lending Pools (LP). This was achieved by leveraging an existing formal model for LPs, the Maude verification environment and the MultiVeStA statistical analyser. The tool supports several analyses including reachability analysis, LTL model checking and statistical model checking. In this paper we show how the tool can be used to analyse several parameters of LPs that are fundamental to assess and predict their behaviour. In particular, we use statistical analysis to search for threshold and reward parameters that minimize the risk of unrecoverable loans.

Xiaofeng Chen, Lu Zhang, Yijian Zhang, Jingyi Du · 6 authors

Blockchain and Distributed Ledger Technology (DLT) are becoming important driving forces for a new round of scientific and technological development. The construction of blockchain information infrastructure has risen to a national strategic height. The industrial applications of Blockchain and DLT are developing and landing rapidly. At present, it has included many fields such as finance, government affairs and justice. At the same time, blockchain and DLT will have a far-reaching impact on international scientific, technological and economic development. For the healthy and orderly development of the blockchain and DLT industry, blockchain and DLT standards are being drafted, released and implemented one after another. This paper analyzes the current situation of international blockchain and DLT standardization and arranges the current situation of blockchain standardization working group in ISO. This paper introduces the organizational structure of ISO and the specific work of the working group, arranges all the blockchain related standards currently under development by ISO, and makes relevant analysis on the standard stage, standard content and standard classification; At the same time, it defines the direction of in-depth participation in ISO international standards and helps the development of international standardization of blockchain and DLT.

Jens Ducrée, Martin Codyre, Ray Walshe, Sönke Barting

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) &amp;ldquo;blockchain&amp;rdquo; to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (&amp;ldquo;TradSci&amp;rdquo;), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific &amp;ldquo;metaverse&amp;rdquo;. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (&amp;ldquo;NFTs&amp;rdquo;). The so enabled, highly integrative approach, termed decentralized science (&amp;ldquo;DeSci&amp;rdquo;), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.

Babu Pillai, Kamanashis Biswas, Zhé Hóu, Vallipuram Muthukkumarasamy

The Level of Conceptual Interoperability Model (LCIM) is a widely used framework that represents inter-relationship among interoperability and composability of different information systems. Although this model has been successfully applied to various domains such as cybernetics and informatics, there are many challenges in directly adopting the model for blockchain-based systems. This paper identifies those challenges and proposes a new Level of Conceptual Interoperability Model for blockchain systems based on the original LCIM. We define five different levels of interoperability for blockchain-based systems and theoretically evaluate the level of interoperability (LOI) achieved by different blockchain networks. The evaluation outcomes show that there exists technical interoperability (Level 1) between Bitcoin and Ethereum networks, whereas Solana and Binance achieve pragmatic interoperability (Level 4) by conveying state changes with the Ethereum network and Polkadot achieve dynamic interoperability (level 5) by suitably conveying state changes within the ecosystem of its networks. We present case studies that demonstrate how the proposed LCIM for blockchain systems map various real-world applications to their respective levels.

Jin Xing Lim, Barnabé Monnot, Georgios Piliouras

Scientific research, and particularly research in mathematics, is arguably one of the crowning achievements of our collective human intellect. Its creation increasingly requires collaboration between multiple researchers with different and sometimes complementary backgrounds. On the other hand, its verification requires a careful matching between the expertise of reviewers and authors. Unfortunately, errors do happen and sometimes are only corrected many years after they appear in print, if at all. Nevertheless, at least when it comes to mathematical research, computer-verified formal proofs are possible as a final arbiter of mathematical truth, however, they are hard to produce even for relatively simple statements. Hence, such approaches typically lack far behind the current research frontier. In our work we present a novel blockchain-based system to tackle such issues. We discuss how the use of a combination of existing technologies such as decentralized file storage, review systems, smart contracts, non-fungible tokens (NFTs) and proof assistants can speed up and improve the quality and collaboration of mathematical research. As a proof of concept, we also design a mechanism to incentivize collaborative work and credit sharing that encourages provers to share their partial progresses without delay. Simultaneously, our mechanism incentivizes expert reviewers to challenge publicized proofs with formal proofs enabling dispute resolution, if needed.

Changhao Chenli, Wenyi Tang, Frank Gomulka, Taeho Jung

No abstract is available for this record.

I P Raghesh Kumar, Rithin Varghese, Nandu Nagesh, Vyshnav Sasidharan · 5 authors

A majority of the research on accident detection systems involves increasing the precision at which it can be detected. This approach proposes a test-bed for a vehicular incident detection system that uses real time data collected from OBD -II port of automobiles and later passed on to Edge devices within vehicles to detect an accident or other rash driving behavior patterns. Machine learning algorithms are developed and deployed onto edge devices like Jetsonnano for real time pattern recognition. Once an incident like accident or rash driving is triggered, the data is cached and written onto Inter Planetary File Systems (IPFS) a distributed file storage system and put onto Ethereum Blockchain for later analysis and documentation by various stakeholders like police, RTO, law, forensic team etc. Blockchain acts as an immutable ledger providing proof of all incidents. Further using the obtained data, dashboards of the incident can be generated for further visualization and understandability to the concerned stakeholder

Fei–Yue Wang, Wenwen Ding, Xiao Wang, Jonathan M. Garibaldi · 7 authors

This article discusses the impact and significance of the autonomous science movement and the role and potential uses of intelligent technology in DAO-based decentralized science (DeSci) organizations and operations. What is DeSci? How does it relate the science of team science? What are its potential contributions to multidisciplinary, interdisciplinary, and/or transdisciplinary studies? Does it have any correspondence to the social movement organizations in traditional social sciences or the cyber movement organizations in the new digital age? Particularly, issues on DeSci to current professional communities, such as IEEE and its societies, conferences, and publications, are addressed, and the effort for the framework and process of DAO-based DeSci for free, fair, and responsibility sensitive sciences is reviewed.

Visara Urovi, Vikas Jaiman, Arno Angerer, Michel Dumontier

Easy access to data is one of the main avenues to accelerate scientific research. As a key element of scientific innovations, data sharing allows the reproduction of results and helps prevent data fabrication, falsification, and misuse. Although the research benefits from data reuse are widely acknowledged, the data collections existing today are still kept in silos. Indeed, monitoring what happens to data once they have been handed to a third party is currently not feasible within the current data sharing practices. We propose a blockchain-based system to trace data collections and potentially create a more trustworthy data sharing process. In this paper, we present the LUCE (License accoUntability and CompliancE) architecture as a decentralized blockchain-based platform supporting data sharing and reuse. LUCE is designed to provide full transparency on what happens to the data after they are shared with third parties. The contributions of this work consist of i) the design of a decentralized data sharing solution with accountability and compliance by design and ii) the inclusion of a dynamic consent model for personalized data sharing preferences and for enabling legal compliance mechanisms. We test the scalability of the platform in a real-time environment where a growing number of users access and reuse different datasets. Compared to existing data sharing solutions, LUCE provides transparency over data sharing practices, enables data reuse, and supports regulatory requirements. The experimentation shows that the platform can be scaled for a large number of users.

Timotej Knez, Domen Gašperlin, Marko Bajec, Slavko Žitnik

Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.

Mohammed Sadik Abdullah

AI collaboration increasingly spans untrusted, heterogeneous nodes from edge devices to multi-clouds raising acute concerns around privacy, integrity, and verifiability of shared models and updates. This paper proposes a secure distributed computing framework that unifies privacy-preserving learning, verifiable coordination, and incentive-aligned governance for decentralized AI model sharing. The architecture composes federated and peer-to-peer training with secure aggregation, differential privacy, and hardware-backed confidential computing to prevent data leakage while mitigating gradient inversion risks. Model provenance, access control, and policy enforcement are anchored via a lightweight, append-only ledger with decentralized identifiers, enabling auditability without central authorities. To counter poisoning, backdoors, and Sybil attacks, the framework integrates robust aggregation, reputation-weighted participation, and update attestation with zero-knowledge proofs for selective disclosure. A resource-aware scheduler adapts to edge variability using gossip-based dissemination, opportunistic bandwidth utilization, and erasure-coded checkpoints to preserve liveness under churn. Interoperability is ensured through portable model artifacts (e.g., ONNX), secure enclaves for cross-framework execution, and privacy budgets tracked as first-class governance assets. We outline threat models, compliance hooks for jurisdictional constraints, and a token-free contribution accounting mechanism that rewards data quality and validation work. Simulated and real-world deployments illustrate improved end-to-end trust, reduced coordination overhead, and resilient performance under adversarial conditions, positioning the framework as a practical substrate for open, secure, and accountable AI collaboration in decentralized environments