Purpose. The purpose of this article is to conduct a philosophical-legal analysis of the evolving capabilities and structural constraints within smart contracts, viewed through the lens of practical philosophy. This analysis seeks to comprehend the contradictory nature of the digital transformation of legal reality.Design / Method / Approach: This work employs an interdisciplinary approach, integrating philosophical-legal analysis with elements of the dialectical method and critical research on technological innovations. The methodological foundation comprises concepts of practical philosophy, the theory of legal autonomy, and approaches from the critical theory of technology.Findings. The study reveals the evolving nature of smart contracts, which manifests in the simultaneous expansion of opportunities through the elimination of institutional intermediaries and the creation of new structural limitations via algorithmic rigidity. It is established that technological liberation from traditional forms of dependence generates qualitatively new forms of technological dependence. Furthermore, it is shown that technical complexity creates new forms of legal inequality between individuals with varying levels of technical literacy.Theoretical implications. The research results contribute to the development of philosophical-legal theory in the digital age by conceptualizing the dialectical nature of technological transformations in law, thereby enriching the understanding of the contradictions between technological efficiency and legal flexibility.Practical implications. The findings provide a theoretical basis for developing balanced regulatory approaches to smart contracts that account for their contradictory nature and for formulating principles of responsible design of technological systems.Originality / Value. A dialectical approach is proposed for analyzing smart contracts as a unity of opposites, where the expansion of opportunities and structural limitations mutually condition each other within a single technological phenomenon. The conceptual framework is expanded for understanding the contradictory nature of the digital transformation of legal reality through the prism of practical philosophy.Research limitations / Future research. Further in-depth research is needed on the philosophical-legal aspects of determinism in the execution of smart contracts, including an analysis of the transformation of legal temporality and issues of unforeseen circumstances in algorithmic systems.Paper type. Theoretical.
The proliferation of autonomous systems, from collaborative AI agents to decentralized decision-making units, has introduced a new dimension of machine-to-machine interaction. However, the communication between intelligent agents, especially in critical domains such as cybersecurity, defense, and finance, remains vulnerable to interception, spoofing, impersonation, and logic poisoning. This paper proposes a novel security-first communication framework designed specifically for AI-to-AI interaction. We explore foundational agent communication models, identify key security challenges in inter-agent exchange, and develop a comprehensive architecture embedding authentication, message integrity, encryption, trust verification, and agent self-defense mechanisms. A prototype implementation demonstrates how secure session establishment, encrypted payload exchange, and identity attestation can be achieved using modern cryptographic methods in a distributed agent swarm. Evaluations highlight both the threat landscape and performance implications of adding secure layers to agent dialogues. This work provides a blueprint for designing resilient AI systems that can securely reason, coordinate, and act in untrusted environments.
Driven by blockchain technology, numerous industries are increasingly adopting smart contracts to enhance efficiency, reduce costs, and improve transparency. As a result, ensuring the security of smart contracts has become critical. Traditional detection methods often suffer from low efficiency, are prone to missing complex vulnerabilities, and have limited accuracy. Although deep learning approaches address some of these challenges, issues with both accuracy and efficiency remain in current solutions. To overcome these limitations, this paper proposes a symmetry-inspired solution that harmonizes bidirectional and generative semantic patterns. First, we generate distinct feature extraction segments for different vulnerabilities. We then use the Bidirectional Encoder Representations from Transformers (BERT) module to extract original semantic features from these segments and the Generative Pre-trained Transformer (GPT) module to extract generative semantic features. Finally, the two sets of semantic features are fused using a multi-attention mechanism and input into a classifier for result prediction. Our method was tested on three datasets, achieving F1 scores of 93.33%, 93.65%, and 92.31%, respectively. The results demonstrate that our approach outperforms most existing methods in smart contract detection.
Qazi Muhammad Osama, Usman Ali, Tahira Ali, Danish Ali
The rapid rate of technological development necessitates a comprehensive and morally sound framework to guarantee acceptable integration between commercial innovation, mechanical systems, and artificial intelligence (AI). This review article presents a unified approach to integrated technology management by examining the intersection of future business models, mechanical sustainability, and AI ethics. The study emphasizes the significance of matching innovation with long-term environmental and societal objectives by analyzing the ethical ramifications of AI deployment—such as algorithmic bias, transparency, and accountability—as well as developments in sustainable mechanical engineering, such as eco-efficient machinery, lifecycle optimization, and circular manufacturing. Additionally, it explores revolutionary business models that are changing value generation and operational efficiency in the contemporary technology ecosystem, including platform economies, digital twins, decentralized finance (DeFi), and Industry 5.0 frameworks. Cross-sectoral synergies that support scalability, equality, and resilience are given particular attention. In order to balance the ethical use of AI with sustainable engineering methods and progressive entrepreneurship, the assessment also emphasizes the crucial roles that legislative frameworks, data governance, and stakeholder collaboration play. In the end, this integrated viewpoint offers practical advice to academics, politicians, and business executives working to create technology systems that are nimble, sustainable, and responsible in a time of complexity and ongoing innovation.
The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny.
As governmental health information systems become increasingly digitized, the need for collaborative analytics across decentralized regions has intensified.However, privacy concerns, regulatory constraints, and infrastructure disparities have limited the extent to which sensitive health data can be aggregated and analyzed across jurisdictions.This paper explores the integration of blockchain technology with federated learning (FL) to enable privacy-preserving data analytics across distributed governmental health information systems.By combining FL's decentralized model training capabilities with blockchain's immutable, transparent ledger and consensus mechanisms, the proposed framework ensures secure, auditable, and policy-compliant data collaboration without requiring raw data exchange.The framework leverages smart contracts to automate access control, consensus validation, and compliance enforcement among participating health institutions.Each node (representing a governmental health entity) trains models locally and shares only encrypted model parameters, which are validated and recorded on the blockchain.This eliminates the need for centralized authorities and reduces the risk of data leakage or manipulation.A core contribution of this work lies in addressing public-sector constraints such as legacy infrastructure, heterogeneous data standards, and institutional trust gaps through a modular, interoperable design.The system includes support for dynamic node participation, real-time updates, and compatibility with health data standards such as HL7 and FHIR.Use-case simulations across municipal, regional, and national health departments demonstrate improved efficiency in outbreak prediction, chronic disease surveillance, and population-level risk stratification while maintaining strict compliance with data protection regulations.This paper advances a scalable and trustworthy architecture for cross-border health collaboration, offering a blueprint for digital public health infrastructures in the age of data sovereignty and distributed intelligence.
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access. By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning. ETrace implements pattern-matching to establish causal links between transaction behavior patterns and known attack behaviors. Furthermore, we validate the effectiveness of ETrace through preliminary experimental results.
Smart contracts are self-executing programs that facilitate trustless transactions between multiple parties, most commonly deployed on the Ethereum blockchain. They have become integral to decentralized applications in areas such as voting, digital agreements, and financial systems. However, the immutable and transparent nature of smart contracts makes security vulnerabilities especially critical, as deployed contracts cannot be modified. Security flaws have led to substantial financial losses, underscoring the need for robust verification before deployment. This survey presents a comprehensive review of the state of the art in smart contract security verification, with a focus on Ethereum. We analyze a wide range of verification methods, including static and dynamic analysis, formal verification, and machine learning, and evaluate 62 open-source tools across their detection accuracy, efficiency, and usability. In addition, we highlight emerging trends, challenges, and the need for cross-methodological integration and benchmarking. Our findings aim to guide researchers, developers, and security auditors in selecting and advancing effective verification approaches for building secure and reliable smart contracts.
The rapid integration of artificial intelligence (AI) into high-stakes domains such as healthcare, finance, defense, and governance has created an urgent demand for transparent, auditable, and tamper-resistant decision-making frameworks. While AI models, particularly deep learning architectures, provide unparalleled predictive power, their opaque "black-box" nature often results in accountability gaps, regulatory non-compliance, and ethical challenges. Traditional logging mechanisms fail to capture the complexity and sensitivity of AI-driven decisions, especially in multi-stakeholder ecosystems. Blockchain technology, with its inherent features of immutability, decentralization, and verifiability, presents itself as a transformative solution to this problem. This manuscript proposes and evaluates blockchain-based logging systems for AI auditing, highlighting how distributed ledgers can establish immutable trails of model inputs, intermediate reasoning, and final outputs. The study conducts a comprehensive literature review on AI auditability, trust mechanisms, and blockchain applications, followed by a methodological framework integrating permissioned blockchains with explainable AI (XAI). A statistical analysis is presented to compare blockchain-logging versus traditional logging systems in terms of latency, transparency, energy consumption, scalability, and regulatory compliance. Results indicate that blockchain-based logging improves transparency by 78%, strengthens compliance traceability by 65%, and reduces auditing disputes by 52%, albeit at a moderate computational cost. The paper concludes that blockchain-based logging is not merely a technical enhancement but a regulatory and ethical necessity for next-generation AI systems. Future research directions include hybrid blockchain models, privacy-preserving logging protocols, and AI-governed adaptive consensus mechanisms.
A persistent semantic gap separates the low-level revert data emitted by smart contracts from the high-level explanations Web3 users need when a transaction fails. Existing automated analyzers treat such reverts as hints of hidden vulnerabilities and do not tell users what actually went wrong. To close this gap and give users useful feedback, I present ErrorExplainer, an automated error-explanation framework rather than another bug detector. ErrorExplainer takes a novel two-phase approach. A lightweight static analysis of verified source code lifts every transaction-reverting statement into a canonical error representation of an origin function, a guard condition, and an expected error message. At runtime, when a failure occurs, ErrorExplainer first checks the invariant part of the error data with the error representation of the called function. If no hit appears, it expands the candidates to call traces until a match is found and then shows the matched record as a clear, human-readable explanation. The evaluation results show that ErrorExplainer could effectively identify 6284 normalized error records from a reverting-related dataset of SC-Bench. The high information completeness (0.952) and matching fitness (0.954 and 0.604 at the function and trace levels, respectively) indicate that the extracted error context of ErrorExplainer can provide more understandable information to users on failed operations.
With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.
Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts.
We develop and apply epistemic tests to various decentralized governance methods as well as to study the impact of participation. These tests probe the ability to reach a correct outcome when there is one. We find that partial abstention is a strong governance method from an epistemic standpoint compared to alternatives such as various forms of ``transfer delegation" in which voters explicitly transfer some or all of their voting rights to others. We make a stronger case for multi-step transfer delegation than is present in previous work but also demonstrate that transfer delegation has inherent epistemic weaknesses. We show that enhanced direct participation, voters exercising their own voting rights, can have a variety of epistemic impacts, some very negative. We identify governance conditions under which additional direct participation is guaranteed to do no epistemic harm and is likely to increase the probability of making correct decisions. In light of the epistemic challenges of voting-based decentralized governance, we consider the possible supplementary use of prediction markets, auctions, and AI agents to improve outcomes. All these results are significant because epistemic performance matters if entities such as DAOs (decentralized autonomous organizations) wish to compete with organizations that are more centralized.
Alex Wong, Duncan McFarlane, Charlotte Ellarby, M.B. Lee · 5 authors
Twenty-five years ago, the specification of the Intelligent Product was established, envisaging real-time connectivity that not only enables products to gather accurate data about themselves but also allows them to assess and influence their own destiny. Early work by the Auto-ID project focused on creating a single, open-standard repository for storing and retrieving product information, laying a foundation for scalable connectivity. A decade later, the approach was revisited in light of low-cost RFID systems that promised a low-cost link between physical goods and networked information environments. Since then, advances in blockchain, Web3, and artificial intelligence have introduced unprecedented levels of resilience, consensus, and autonomy. By leveraging decentralised identity, blockchain-based product information and history, and intelligent AI-to-AI collaboration, this paper examines these developments and outlines a new specification for the Intelligent Product 3.0, illustrating how decentralised and AI-driven capabilities facilitate seamless interaction between physical AI and everyday products.
Integrating AI and blockchain technologies holds significant potential for enhancing e-governance, particularly in improving predictive policy execution within smart cities. This study conducts a comprehensive review and bibliometric analysis of existing literature to identify trends, key publications, and research gaps. Using peer-reviewed articles indexed by Scopus and published between 2019 and 2024, we observe a significant rise in research output, focusing on the separate applications of AI and blockchain in e-governance. Key themes identified include enhanced transparency, efficiency in public services, and concerns related to data privacy. However, our analysis uncovers a clear gap in empirical studies addressing the combined use of AI and blockchain technologies. The bibliometric coupling map reveals central clusters around “smart city” and “blockchain,” while topics such as “sustainability” and “climate change” show significant impact, highlighting their relevance to governance. Additionally, the study identifies a lack of cross-disciplinary research, emphasizing the need for future interdisciplinary collaborations. Despite the insights gained, the study is constrained by its reliance on bibliometric methods, which may not capture the complexities of real-world technology integration. Future research should prioritize longitudinal case studies and pilot projects to address regulatory, ethical, and practical challenges, contributing to the responsible adoption of AI and blockchain in digital governance.
With the rapid expansion of blockchain applications, smart contracts are becoming increasingly complex, making the automated detection of contract vulnerabilities more critical than ever. Large language models, due to their advanced code comprehensive ability, are considered to have the potential to undertake the task of automated software vulnerability discovery. Although there have been empirical studies on ChatGPT's automated discovery of contract vulnerabilities, the current empirical research has not addressed how well ChatGPT can detect logical vulnerabilities in smart contracts or whether ChatGPT's detection performance for logical vulnerabilities can be improved. To fill this gap, this study collected and organized seven types of logical vulnerability source codes from 6165 real smart contract audit reports and three datasets, such as Web3Bugs, and used this database to validate ChatGPT's detection capability for logical vulnerabilities. To improve ChatGPT's accuracy in detecting logical vulnerabilities, we fine-tuned ChatGPT with a dataset marked with a specific method, achieving an average accuracy rate of 95% for single vulnerability detection per training session. We improved the original marking method to increase further the number of vulnerabilities that a single model can detect. We used a specific completion marking format, ultimately enabling ChatGPT to detect various logical vulnerabilities. In terms of enhancing model scalability, we found a special training set marking method that allows for the addition of detectable vulnerability types through secondary training.
Primavera De Filippi, Morshed Mannan, Wessel Reijers
ABSTRACT Emerging technologies pose many new challenges for regulation and governance on a global scale. With the advent of distributed communication networks like the Internet and decentralized ledger technologies like blockchain, new platforms emerged, disrupting existing power dynamics and bringing about new claims of sovereignty from the private sector. This special issue addresses a gap in the literature by focusing the discourse on the issue of trust and confidence in the digital realm. In particular, looking at the evolution of the web (from Web 1.0, to Web 2.0, and then Web 3), this article analyses how every iteration reflects a different way of dealing with the problem of trust online, resulting in a different regulation and governance landscape. Technology is often regarded as a new lever of regulation, attempting to resolve the problem of “trust” online, either through the introduction of a new trusted authority (Web 2.0) or through the introduction of technological guarantees that provide more assurance—or “confidence”—in the way interactions can be operationalized (Web 3). Yet, each of these technologies also introduce new risks and governance costs, ultimately shifting the problem of trust in a new direction rather than resolving it or removing the need for trust altogether. The main contribution of the articles in this special issue is providing a better understanding of the trust challenges faced and posed by emerging technologies and demonstrating how they affect institutional governance—in both theory and practice—with a view to help policymakers find appropriate answers to these challenges.
Nishant Jagannath, Christopher Kevin Wong, Braden Mcgrath, Md. Faruque Hossain · 7 authors
The rapid advancement of artificial intelligence (AI) has brought about sophisticated models capable of various tasks ranging from image recognition to natural language processing. As these models continue to grow in complexity, ensuring their trustworthiness and transparency becomes critical, particularly in decentralized environments where traditional trust mechanisms are absent. This paper addresses the challenge of verifying personalized AI models in such environments, focusing on their integrity and privacy. We propose a novel framework that integrates zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) with Chainlink decentralized oracles to verify AI model performance claims on blockchain platforms. Our key contribution lies in integrating zk-SNARKs with Chainlink oracles to securely fetch and verify external data to enable trustless verification of AI models on a blockchain. Our approach addresses the limitations of using unverified external data for AI verification on the blockchain while preserving sensitive information of AI models and enhancing transparency. We demonstrate our methodology with a linear regression model predicting Bitcoin prices using on-chain data verified on the Sepolia testnet. Our results indicate the framework's efficacy, with key metrics including proof generation taking an average of 233.63 seconds and verification time of 61.50 seconds. This research paves the way for transparent and trustless verification processes in blockchain-enabled AI ecosystems, addressing key challenges such as model integrity and model privacy protection. The proposed framework, while exemplified with linear regression, is designed for broader applicability across more complex AI models, setting the stage for future advancements in transparent AI verification.
A blockchain is a trustless system in an environment populated by untrusted peers. Code deployed in blockchain as a smart contract should be cautious when invoking contracts of other peers as they might introduce several risks and unexpected issues. This paper presents an information flow-based approach for detecting cross-contract invocations to untrusted contracts, written in general-purpose languages, that could lead to arbitrary code executions and store any results coming from them. The analysis is implemented in GoLiSA, a static analyzer for Go. Our experimental results show that GoLiSA is able to detect all vulnerabilities related to untrusted cross-contract invocations on a significant benchmark suite of smart contracts written in Go for Hyperledger Fabric, an enterprise framework for blockchain solutions.
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchmarks, the GOD model measures how well assistants can anticipate user needs-such as suggesting gifts-while protecting user data and autonomy. Functioning like an AI school, it addresses the cold start problem by simulating user queries and employing a curriculum-based approach to refine the performance of each assistant. Running within a Trusted Execution Environment (TEE), it safeguards user data while applying reinforcement and imitation learning to refine AI recommendations. A token-based incentive system encourages users to share data securely, creating a data flywheel that drives continuous improvement. Specifically, users mine with their data, and the mining rate is determined by GOD's evaluation of how well their AI assistant understands them across categories such as shopping, social interactions, productivity, trading, and Web3. By integrating privacy, personalization, and trust, the GOD model provides a scalable, responsible path for advancing personal AI assistants. For community collaboration, part of the framework is open-sourced at https://github.com/PIN-AI/God-Model.
Owing to the swift advancement of technology and the unfamiliarity of the execution environment, the development of Solidity smart contracts from scratch often results in significant vulnerabilities.In contrast, automated code generation enhances productivity, minimizes development time, and enables developers to focus on high-level tasks and fundamental logic.In consideration of these two viewpoints, this paper examines the utilization of large language models (LLMs) for the automatic generation of Solidity smart contracts based on specified criteria, while simultaneously ensuring the elimination of vulnerabilities through a novel masking strategy.To achieve this, we propose SolGen, a framework for generating secure Solidity smart contract code using LLMs.We assess the performance of existing LLMs (i.e.ChatGPT and Meta AI) for secure Solidity code generation.Our research indicates that ChatGPT outperforms Meta AI in performance, yielding a greater percentage of syntactically accurate and secure code.Additionally, we examine the impact of temperature adjustment on the security of generated contracts using an open-source LLM, Llama3.Our findings suggest that a temperature setting of 0.7 is optimal for the generation of Solidity code, considerably exceeding the performance of both lower and higher settings (0.1 and 1.2), especially with regard to the compilability of the code.
This article explores the transformative potential of blockchain technology in maintaining test data integrity across regulated industries, particularly in finance, healthcare, and pharmaceuticals. The article examines how blockchain's inherent characteristics address critical challenges in data security, compliance, and operational efficiency. Through analysis of implementation strategies, industry-specific applications, and emerging trends, the article demonstrates how blockchain technology revolutionizes test data management through immutable record-keeping, decentralized architecture, and smart contract automation. The article indicates significant improvements in data security, compliance management, and operational efficiency across all examined sectors, while highlighting the importance of structured implementation approaches and industry-specific considerations.
Abstract Decentralized Autonomous Organizations (DAOs) are attracting interest from various disciplines, particularly business and economics, and computer science. However, much like the parable of the blind men and the elephant, where each observer sees only part of the phenomenon, DAO research has largely remained fragmented across disciplines, limiting a comprehensive understanding of the potential of DAOs. This paper investigates to which extent DAO scholarship has achieved meaningful interdisciplinary integration. We address this question through an analysis of knowledge flows between Business and Economics and Computer Science, using citation network analysis, topic modelling, and outlet analysis. We find that while DAOs generate vibrant interdisciplinary discourse, the interactions remain predominantly applied and case-driven, with limited theoretical integration. By mapping interdisciplinary exchanges, we highlight key gaps and opportunities for greater synthesis across fields. We argue that strengthening the alignment between organizational and technical insights is crucial for advancing DAO research and fostering a more cohesive interdisciplinary framework.
Bishwo Prakash Pokharel, Naresh Kshetri, Suresh Raj Sharma, S. Paudel
The COVID-19 pandemic exposed critical vulnerabilities in global healthcare systems, particularly in data security and interoperability. This paper introduces the blockHealthSecure Framework, which integrates blockchain technology with advanced cybersecurity measures to address these weaknesses and build resilient post-pandemic healthcare systems. Blockchain’s decentralized and immutable architecture enhances the accuracy, transparency, and protection of electronic medical records (EMRs) and sensitive healthcare data. Additionally, it facilitates seamless and secure data sharing among healthcare providers, addressing long-standing interoperability challenges. This study explores the challenges and benefits of blockchain integration in healthcare, with a focus on regulatory and ethical considerations such as HIPAA and GDPR compliance. Key contributions include detailed case studies and examples that demonstrate blockchain’s ability to mitigate risks like ransomware, insider threats, and data breaches. This framework’s design leverages smart contracts, cryptographic hashing, and zero-trust architecture to ensure secure data management and proactive threat mitigation. The findings emphasize the framework’s potential to enhance data security, improve system adaptability, and support regulatory compliance in the face of evolving healthcare challenges. By bridging existing gaps in healthcare cybersecurity, the blockHealthSecure Framework offers a scalable, future-proof solution for safeguarding health outcomes and preparing for global health crises.