This article explores how blockchain technology fundamentally transforms identity management in payment ecosystems through decentralized identity frameworks. The paper examines how distributed ledger technology addresses traditional challenges including security vulnerabilities, inefficient KYC processes, and privacy concerns. Self-Sovereign Identity principles empower users with control over their personal data through digital wallets and verifiable credentials that enable selective disclosure. The implementation architecture integrates identity registration, digital wallet infrastructure, verification protocols, smart contract governance, and secure transaction finalization. This approach creates significant benefits across the payment ecosystem – financial institutions experience reduced fraud and streamlined compliance, merchants benefit from higher conversion rates and reduced liability, while consumers gain enhanced privacy and security. Despite these advantages, the paper acknowledges challenges including standardization requirements, regulatory alignment, credential recovery mechanisms, and scalability considerations that must be addressed for widespread adoption of decentralized identity in payment systems.
Klaus Grobys, James W. Kolari, Davide Sandretto, Syed Jawad Hussain Shahzad · 5 authors
Abstract This paper explores the tail behavior of cryptocurrency momentum strategies and the profitability of volatility-managed momentum portfolios. Our main results derived from using a sample of large-cap cryptocurrencies and equal-weighted momentum portfolios indicate that cryptocurrency momentum is subject to severe crashes. Even a single cryptocurrency can cause insignificant momentum portfolio returns. In line with the literature on volatility-managing equity portfolios, our findings suggest that volatility management is a useful tool for mitigating cryptocurrency momentum crashes. Further corroborative evidence suggests that cryptocurrency momentum appears to be a phenomenon associated with large-cap cryptocurrencies.
The economic and legal problems of the development of innovative technologies of the digital economy (using the example of cryptocurrency and blockchain) are studied: the issue of state regulation of cryptocurrency, the possibilities of its creation, use and limitations, the importance and prospects of their use in the modern world and in Ukraine. Cryptocurrency is one of the most promising technologies of the digital economy, which is actively developing every year, the volume of its circulation is increasing. Ukraine isamong the world leaders in the use of cryptocurrencies. In Ukraine, the Law «On Virtual Assets» was adopted, which regulates the procedure for the emergence, change, and termination of rights to a new object of civil law for Ukrainian legislation — cryptocurrency. It is shown that blockchain technology is the main technology of digitalization of social relations and legal processes in most developed legal systems of the world, which is used in the field of cryptocurrencies, smart contracts, registration of intellectual property (IP), ecommerce, and the Internet things, the economy of joint participation, etc. The connection between patents and cryptocurrency was revealed. The patenting of Blockchain, Crypto and DeFi technologies was analysed. Recently, many IP offices have revised the norms of patent legislation and the rules of examination of patent applications in order to recognize the patentability of these technologies. The best experience of legal regulation, inventive activity and the dynamics of patenting of inventions in this area in different jurisdictions (USA, EPO, China, France, Japan, South Korea), technological trends and regulatory problems are analysed. Recommendations on increasing the effectiveness of activities in this area in Ukraine have been provided. It is necessary to implement the rules of the EPO Guidelines (Guidelines for Examination) on computer-implemented inventions in the Rules for drawing up, submitting and examining an application for an invention and an application for a utility model.Cryptocurrencies and blockchain have significant potential, many companies have invested heavily in these fields, so countries' patent laws must ensure the protection of the respective investments.
Blockchain technology, a decentralized and immutable ledger, has transformed identity and access management (IAM) by enhancing security, privacy, and trust in digital ecosystems. Ensuring safe authentication and data integrity is made possible by its integration with sophisticated cryptographic techniques like zero-knowledge proofs (ZKPs) and public- key infrastructure (PKI). Other methods include verifiable credentials (VCs) and decentralized identifiers (DIDs). This paper provides a comprehensive analysis of blockchain-based IAM systems, comparing leading blockchain platforms, including Ethereum, Hyperledger Indy, IOTA, and IoTeX, in identity management. The role of blockchain in mitigating identity-related threats, such as identity theft and unauthorized access, is explored through decentralization, immutability, and smart contract automation. Additionally, key security enhancements, including cryptographic mechanisms that strengthen decentralized identity solutions and privacy-preserving authentication, are examined. The potential of blockchain to establish a self-sovereign identity framework that fosters trust, scalability, and security in digital identity ecosystems is highlighted, paving the way for the next generation of identity management solutions.
This article presents a comprehensive analysis of contemporary cyber threats to the financial sector, emphasizing their impact on the stability of financial institutions and payment systems. The study addresses the primary vectors of cyberattacks—phishing, DDoS, malware, targeted APT attacks, and insider threats. Drawing on the experiences of Ukrainian and international financial institutions, the paper identifies key principles for constructing cybersecurity systems, encompassing multi-layered protection, vulnerability management, proper authentication, and incident response planning. Special attention is devoted to artificial intelligence and machine learning as instruments for enhancing cyber resilience. The article also examines the potential for implementing blockchain and decentralized finance (DeFi) within the global financial landscape and the associated information security challenges. The significance of integrated cyber risk management within financial institutions' broader operational risk management framework is underscored. Finally, practical recommendations are offered on optimizing security frameworks, adopting international standards, and bolstering intergovernmental coordination to ensure the financial sector’s long-term resilience in the face of digital transformation.
While the Indian public has grown quite fond of cryptocurrency in recent times, ambiguity still persists; specifically with respect to taxation. The primary reason for the same is unclear regulations. In 2022 India introduced a tax structure for virtual digital assets (VDAs) that included a 30% tax on gains along with a 1% TDS for transactions exceeding specified thresholds. The perplexity emerges primarily from India's ambiguous cryptocurrency tax regulations combined with compliance challenges. The existing framework regrettably fails to achieve both clarity and fairness by indiscriminately categorizing all digital assets as VDAs without distinguishing between cryptocurrencies, utility tokens, and non-fungible tokens (NFTs), which results in additional complications for tax treatment and compliance. This paper suggests reforms such as clearer asset definitions, revised tax rates to promote long-term investment, and simplifying the TDS process. It has called for the establishment of a regulatory authority in order to attain uniformity in taxation and trust of investors, with a view toward bringing alignment between India's cryptocurrency market and the globe.
This article explores the integration of artificial intelligence into fintech risk management frameworks, examining how predictive analytics are revolutionizing risk assessment and mitigation capabilities across the financial services industry. It investigates the evolution of risk management within the rapidly changing fintech landscape, highlighting how traditional approaches prove increasingly inadequate in addressing complex challenges like real-time fraud detection, cybersecurity threats, alternative credit assessment, cryptocurrency volatility, and decentralized finance liquidity risks. The article presents a comprehensive analysis of AI-powered solutions across key risk domains, including credit risk assessment, fraud detection, and market risk modeling, demonstrating their superior performance compared to conventional methods. It further outlines a structured framework for enterprise AI implementation, addressing the critical dimensions of data infrastructure, model development, operational integration, and continuous adaptation. The article also examines significant implementation challenges related to regulatory compliance, model explainability, data quality, and talent requirements. Finally, it explores emerging trends that will shape the future of AI-driven risk management, including federated learning, quantum computing, automated risk mitigation, and ecosystem-wide risk intelligence capabilities.
Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu · 8 authors
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.
Essossinam Pali, Coffi Cyprien Aholou, François Paul Yatta
Since 2019, Togo has been strengthening financial decentralization through municipalization and the election of municipal councilors. Municipal financial autonomy is a key driver of local governance, allowing municipalities to mobilize their own resources, manage tax and non-tax revenues, and implement development projects. However, despite a legal framework governing local taxation, Togolese municipalities continue to face chronic financial constraints that limit their ability to finance public services and infrastructure. This study examines the mechanisms of financial decentralization in Togo and their contribution to municipal budgets. Using a quantitative approach that combines documentary analysis and interviews with 188 experts and practitioners in local finance, the study identifies the following four primary financing mechanisms: local, national, community-based and international. Among these, own revenues, including tax revenues, non-tax revenues, and revenues from the provision of services, together with government transfers through the Local Authorities Support Fund (FACT) are the main sources of local government finance. However, the results show that several legally defined fiscal instruments remain underutilized or outdated in many municipalities, significantly limiting their effectiveness in mobilizing resources. These results highlight the need to optimize fiscal decentralization strategies in order to strengthen the financial autonomy of municipalities and support sustainable territorial development.
Non-fungible tokens (NFTs) represent a promising application of blockchain technology that can potentially disrupt various sectors, mainly tourism. While there have been conceptual discussions regarding the opportunities and challenges of utilizing NFTs for purposes such as digital souvenirs, ticketing, loyalty programs, and conservation initiatives, there remains a significant need for a robust methodological framework to assess the impact of real-world NFT implementations empirically. This paper presents the methodological foundation of ongoing research. It proposes a comprehensive approach to researching NFT initiatives within the tourism sector, which includes data collection methods, analytical techniques, and the design of a workbench for monitoring key performance indicators (KPIs). The proposed framework combines quantitative and qualitative measures to capture the complex nature of NFT adoption, including financial performance, visitor engagement, user experience, and operational efficiency. By establishing standardized protocols and metrics, the proposed methodology aims to enable cross-study comparisons and contribute to developing the best practices for leveraging NFTs in the tourism industry. The work highlights the potential of NFTs to enhance visitor experiences, generate new revenue streams, and promote destinations as tech-savvy hubs, while also addressing ethical and sustainability concerns. The conclusion emphasizes the importance of a structured approach to evaluating NFTs initiatives, which can provide valuable insights for tourism organizations seeking to innovate and remain competitive in a digital landscape. Future research should focus on validating the framework through real-world case studies, exploring additional applications of NFTs in tourism, and addressing challenges related to data availability, technological integration, and stakeholder collaboration.
Fran Brahimi, Mariel Frroku, Skënder Uku, Emiljan Mustaqe
A significant part of the literature on fiscal decentralization confirms that the greater the ability of decentralized governments to adapt policies to local preferences and to be innovative in providing public services, the greater the potential for investments and economic growth. This paper examines the dynamic effects and relationship between own source revenues, unconditional transfers, and local public investments. Over the past decades, fiscal and financial decentralization in Albania has made steady progress. However, the increasing responsibilities of local governments have intensified the need to raise the share of local revenues and expenditures relative to GDP and increase revenue from unconditional transfers. Following the administrative-territorial reform, fiscal decentralization has dynamically evolved, boosting local public revenues and granting greater discretion in their use to meet community needs. The specific law on local self-government finances led to increases in both own revenue and revenue from unconditional transfers. Further reforms have improved local public finance management, including local budgeting reforms, enhanced transparency of tax collection and expenditure, and self-assessment and monitoring of local government's financial status. These modernization efforts related to local finances have yielded positive results regarding macroeconomic stability, fund predictability, and transparent use of public funds. Consequently, central and local governments prioritize public investments in infrastructure and sector revitalization in their budgets. Local public investments have risen annually, driven by increased local income from taxes and government transfers. This growth reflects the focus of local and central development policies on addressing infrastructure and logistical challenges. The consolidation of decentralization and stable central budget transfers have created favorable conditions for local governments to implement new policies enhancing service quality and public investment performance.
This article studies modern approaches to organizational management in the digitalization era. Its purpose is to analyse, systematize, and generalize these approaches. The research examines the transformation of organizational management under the influence of economic digitalization in the 21st century. An analysis of the main challenges traditional management models face amid the rapid development of digital technologies, including machine learning, artificial intelligence, big data, and the Internet of Things. The study substantiates the feasibility of shifting from hierarchical to adaptive management models that ensure flexibility, innovativeness, and rapid organizational adaptation to changes in the external environment. The concept of adaptive management as an open system that continuously adjusts its internal processes in response to market demands and technological changes is analyzed. The key factors for the successful functioning of adaptive management are identified: decentralization, delegation of authority, implementation of autonomous workgroups, and the active use of digital technologies for monitoring and analytics. The exploration of data-driven management features revealed its role as a strategic resource for decision-making, enhancing organizational flexibility, and creating added value. The importance of developing a data-driven organizational culture and implementing integrated information systems to establish evidence-based management practices is emphasized. The study also substantiates the role of digital-era leaders in shaping a vision of the digital future of organizations, promoting innovation, fostering a culture of continuous learning, and enhancing companies' digital maturity. It is noted that effective leadership in the digital era is impossible without creating an atmosphere of trust, readiness for change, and the development of employees' digital competencies. The article also analyses flexible management methodologies that have emerged in response to the challenges of the digital economy and have become essential tools for organizational adaptation to rapid environmental changes. Among the most widespread approaches, Agile, Scrum, and Lean are highlighted, which focus on iteration, flexible planning, constant interaction with stakeholders, and rapid response to new requirements. The research concludes that adaptive management, data-driven approaches, digital transformation leadership, and flexible methodologies are the key success factors for organizations in the digital economy. At the same time, modern management in the digital age requires a comprehensive approach that combines technological innovations, new leadership styles, a shift to open organizational models, and knowledge management.
Aishwarya Parab, P. Pradhan, Yogesh Simmhan, Arnab K. Paul
The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trustworthy environment for storage and retrieval. Blockchain, as a distributed and immutable ledger, offers a promising solution to address these challenges. This short paper studies the feasibility of a blockchain-based framework for secure data storage and retrieval across trusted and untrusted sources, focusing on provenance, storage mechanisms, and smart contract security. Through initial experiments using Hyper Ledger Fabric (HLF), we evaluate the storage efficiency, scalability, and feasibility of the proposed approach. This study serves as a motivation for future research to develop a comprehensive blockchain-based storage and retrieval framework.
Blockchain technology has emerged as a transformative paradigm for decentralized and secure data management across diverse application domains, including healthcare, supply chain management, and the Internet of Things. Its core features, such as decentralization, immutability, and auditability, achieved through distributed consensus algorithms and cryptographic techniques, offer significant advantages for multi-stakeholder applications requiring transparency and trust. However, the inherent complexity and security-critical nature of blockchain systems necessitate rigorous analysis and verification to ensure their correctness, reliability, and resilience against potential vulnerabilities.
The unmanned aerial vehicle (UAV) network has gained significant attentions in recent years due to its various applications. However, the traffic security becomes the key threatening public safety issue in an emergency rescue system due to the increasing vulnerability of UAVs to cyber attacks in environments with high heterogeneities. Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology. Specifically, SDN separates the control and data plane to enhance the network manageability and security. Meanwhile, the blockchain provides decentralized identity authentication and data security records. Beisdes, a complete security architecture requires an effective mechanism to detect the time-series based abnormal traffic. Thus, an integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD. Finally, the simulation results show that the proposed CTranATD algorithm is effective and outperforms the individual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.
By July 2025, smart contracts collectively manage roughly $120 billion in assets. With Solidity remaining the dominant language for smart contract development, the correctness of Solidity compilers has become critically important. However, Solidity compilers are bug-prone, with a recent study revealing that combinations of qualifiers in Solidity programs are the primary cause of compiler crashes, accounting for 40.5% of all historical crashes. While random program generators are widely used for compiler testing, they may be less effective at finding Solidity compiler bugs because they explore the unbounded space of possible programs rather than concentrating on the specific subspace related to bug-prone qualifiers. A promising idea for finding qualifier-related bugs is to bound the search space based on empirical evidence of where such bugs are likely to occur, specifically focusing test generation to target subspaces with rich combinations of qualifiers. To address this, we propose bounded exhaustive random program generation, a novel approach that dynamically bounds the search space, enhancing the likelihood of uncovering Solidity compiler bugs. Specifically, our method bounds the search space by generating valid program templates that abstract programs that use bug-prone qualifiers, and then uses these templates as a basis for compiler testing through exhaustive enumeration of suitable qualifiers. Mechanisms are devised to address technical challenges regarding validity and efficiency. We have implemented our novel generation approach in a new tool, Erwin. We have used Erwin to find and report 26 bugs across two Solidity compilers, solc and solang, and one Solidity static analyzer, slither. Among these, 23 were previously unknown, 18 have been confirmed, and 10 have been fixed. Evaluation results demonstrate that Erwin outperforms state-of-the-art Solidity fuzzers in bug detection.
Utkarsh Azad, Bikash K. Behera, Houbing Song, Ahmed Farouk
Industry 5.0 depends on intelligence, automation, and hyperconnectivity operations for effective and sustainable human-machine collaboration. Pivotal technologies like the Internet of Things (IoT) enable this by facilitating connectivity and data-driven decision-making between cyber-physical devices. As IoT devices are prone to cyberattacks, they can use blockchain to improve transparency in the network and prevent data tampering. However, in some cases, even blockchain networks are vulnerable to Sybil and 51% attacks. This has motivated the development of quantum blockchains that are more resilient to such attacks as they leverage post-quantum cryptographic protocols and secure quantum communication channels. In this work, we develop a quantum binary voting algorithm for the IoT-quantum blockchain frameworks that enables inter-connected devices to reach a consensus on the validity of transactions, even in the presence of potential faults or malicious actors. The correctness of the voting protocol is provided in detail, and the results show that it guarantees the achievement of a consensus securely against all kinds of significant external and internal attacks concerning quantum bit commitment, quantum blockchain, and quantum Byzantine agreement. We also provide an implementation of the voting algorithm with the quantum circuits simulated on the IBM Quantum platform and Simulaqron library.
In today's blockchain landscape, smart contracts are assuming a pivotal role, albeit accompanied by a heightened risk of exploitation by attackers. As smart contracts grow in complexity, vulnerabilities lurking within deeper layers of code become more prevalent. Existing analysis tools primarily focus on data flow and a priori knowledge based on symbolic execution as a test case generation strategy, often falling short in uncovering vulnerabilities nested within intricate conditional statements. To address this challenge, we present ACOFuzz, an advanced fuzzer for Ethereum smart contracts. ACOFuzz employs the ant colony optimization (ACO) algorithm to traverse the control flow graph (CFG) of smart contracts, systematically exploring execution paths and generating test cases. Subsequently, it strategically directs the search towards paths that are more susceptible to vulnerabilities within the CFG, leveraging block coverage data obtained from executing the test cases. In a comprehensive evaluation, we demonstrate that ACOFuzz excels in covering a wider array of paths within a contract while exhibiting enhanced accuracy in pinpointing specific vulnerabilities compared to contemporary fuzzers.
Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principles—when combined with identity-scoped partitioning and cloud-native scalability patterns—enable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.
The research focuses on the “Analysis of the effectiveness of the Special Allocation Fund (SAF) in financing the reconstruction of school facilities in Bogor Regency”. Dana Alokasi Khusus or SAF is one the Indonesian Government’s budget schemes initiated by the Ministry of Education and Culture or the MoEC to assist local governments in reconstructing school buildings that are heavily damaged due to various reasons. However, There have been an increasing number of damaged classrooms over the years during the period 2016 to 2018 in Bogor Regency, one of the worst in Indonesia. The conceptual framework was developed based on three main theoretical frameworks: the theory on measuring the effectiveness of finance policy implementation; the budget accountability in analyzing the effectiveness of public expenditure; and the currently applied government system based on decentralization policy in budget allocation in Indonesia. The research used qualitative methodology, which analyzed the collected data and information descriptively. Semi structured questionnaires were used to conduct interviews to relevant stakeholders. The implementation of building reconstruction based on allocated SAF has been both effective and ineffective. SAF scheme as one of the fiscal policy instruments under decentralization policy for improving the education quality has shown to be less effective due to miss-allocation of budget, improper distribution, lack of participation and transparency, and increased public complaint.The budget accountability of SAF in Bogor Regency has shown that policy on SAF use needs to improve. Recommendation should be directed towards policies to ensure fair process in selecting school as recipient, fair budget distribution, increased public participation and transparency
Konstantinos Sgantzos, Panagiotis Tzavaras, Mohamed Al Hemairy, Eva R. Porras
Within the past five years, and as Artificial Intelligence (AI) increasingly pervades the academic and educational landscape, a delicate balance has emerged between leveraging AI’s transformative potential and safeguarding individual privacy, which needs to be carefully maintained. The preservation of user privacy entails severe financial risks via penalties for the violation of directives such as General Data Protection Regulation (GDPR). This manuscript examines three neoteric approaches to data privacy protection in AI-empowered lifelong education. The first method uses Triple-Entry Accounting (TEA) together with Distributed Ledger Technology (DLT); the second method uses a transaction Merkle tree that can be used as a “proof of existence” so that the users can safeguard their personal information; and the third approach examines the advantages and disadvantages of an offline AI-tutor multimodal model that can operate without internet access. Finally, the ethical implications of deploying such technologies are critically discussed, emphasizing the necessity of achieving privacy while retaining the human factor in education.
Since its creation in 2008, Bitcoin has often been compared to precious metals due to their shared characteristics as safe havens, hedges, and risk diversification tools. This study uses the DCC-GARCH model to analyze dynamic conditional correlations and volatility spillovers between Bitcoin and the returns of gold, copper, silver, and platinum. The findings reveal persistent volatility and clustering in the returns of both Bitcoin and these metals. There is a one-way volatility spillover from gold to Bitcoin, and from Bitcoin to copper, silver, and platinum. Significant dynamic conditional correlations are observed between Bitcoin and both gold and copper, while no significant correlations are found with silver and platinum. These results provide valuable insights for portfolio diversification strategies and inform policymaker decisions in financial markets.
This study investigates the causal relationships between Bitcoin and the US Dollar (USD), Gold, and BIST100 Index as alternative investment instruments. Employing Hong’s variance causality test, the research explores spillover effects in mean and volatility. Using daily data from September 17, 2014, to October 13, 2023, the study reveals a one-way average causality from Bitcoin to BIST100 and the USD. Variance test results show a two-way volatility spillover between Bitcoin and USD, Gold, and BIST100. Hacker-Hatemi-J symmetric causality test detects a one-way causality from Bitcoin to the USD, while Hatemi-J asymmetric test reveals a unidirectional causality from positive Bitcoin shocks to negative shocks of BIST100 and Gold, and bidirectional causality with USD's negative shocks. Additionally, a bidirectional causality exists from Bitcoin's negative shocks to Gold's positive shocks and a unidirectional causality to USD's negative shocks. Recognizing Bitcoin as a financial asset sheds light on its interaction with traditional markets, aiding investors in refining strategies. In summary, this study enhances comprehension of cryptocurrency's role by emphasizing the causal link between Bitcoin and the USD.
With the growth of the Internet of Things (IoT), millions of users, devices, and applications compose a complex and heterogeneous network, which increases the complexity of digital identity management. Traditional centralized digital identity management systems (DIMS) confront single points of failure and privacy leakages. The emergence of blockchain technology presents an opportunity for DIMS to handle the single point of failure problem associated with centralized architectures. However, the transparency inherent in blockchain technology still exposes DIMS to privacy leakages. In this paper, we propose the privacy-protected IoT DIMS (PPID), a novel blockchain-based distributed identity system to protect the privacy of on-chain identity data. The PPID achieves the unlinkability of identity-credential-verification. Specifically, the PPID adopts the Zero Knowledge Proof (ZKP) algorithm and Shamir secret sharing (SSS) to safeguard privacy security, resist replay attacks, and ensure data integrity. Finally, we evaluate the performance of ZKP computation in PPID, as well as the transaction fees of smart contract on the Ethereum blockchain.
Open access
Blockchain Technology Applications and Security
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques