Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruptions, data loss, compromised model integrity, and non-compliance with data protection regulations (e.g., GDPR, CCPA). This paper introduces Differentially Private Resilient Temporal Federated Learning (DP-RTFL), an advanced FL framework designed to ensure training continuity, precise state recovery, and strong data privacy. DP-RTFL integrates local Differential Privacy (LDP) at the client level with resilient temporal state management and integrity verification mechanisms, such as hash-based commitments (referred to as Zero-Knowledge Integrity Proofs or ZKIPs in this context). The framework is particularly suited for critical applications like credit risk assessment using sensitive financial data, aiming to be operationally robust, auditable, and scalable for enterprise AI deployments. The implementation of the DP-RTFL framework is available as open-source.
This article evaluates the regulatory legal landscape of smart contracts within the EU and examines a few essential legal challenges related to the need to harmonize smart contract regulations across the EU. It starts with analysis of some legal and technical aspects of the smart contract term form and arrives at the conclusion that there is no universal and unified term that contains technical aspects of the smart contract. This creates legal uncertainty, as the currently existing legal frameworks in many EU member states are not equipped to address these characteristics of smart contracts.Another issue of importance is the varied approaches to smart contract regulation across the EU member states. The paper reveals that, currently, there is a spectrum of regulatory strategies from pioneering to conservative, and identifies the main obstacles to regulatory harmonization within the EU. Without a common legal framework, a smart contract deemed valid and enforceable in one state may not be recognized in another member state.Finally, the current EU legislation is not specifically designed for smart contracts. However, it impacts their regulation by addressing critical aspects of digital operations like data ownership, access and control. Thus, successful integration of smart contracts into the EUâs regulatory environment will require a concerted effort to address these complex challenges.
The 1980 Vienna Convention on Contracts for the International Sale of Goods (CISG) is currently the law of ninety-seven countries around the world. Part II (Formation of the Contracts) deals with the conclusion of the contract by way of the meeting of minds through offer and acceptance. CISG has been able to adapt to modern electronic means of communication such as email, despite the fact that the means of communication mentioned in the CISG are the ancient telegram and telex.When dealing with the electronic contract of sale, we are referring to those in which the offer and acceptance are made by electronic means, as derived from the rules of the offer and the acceptance under the CISG.In short, we are thinking about computersâtoday also mobile phonesâconnected to a network (internet). From this perspective, every purchase and sale contract under the CISG is capable of being concluded by electronic means following the classic and universal parameter (we find it in all legal systems in the world) of consent through the two declarations of will that give life to the contract, the offer and the acceptance. The offer and acceptance as a mechanism well present in the life of the contract and not only in its formation since other issues such as its modification or termination are observed under those parameters.The Vienna Convention has demonstrated its flexibility by adapting and applying without problems to electronic contracting. Technologies are evolving rapidly and we no longer question the validity of contracts concluded through electronic means but new and interesting perspectives emerge, as well as various legal problems that can be associated with the era of the digital economy, from the use of platforms as an intermediary in the contracting of goods or servicesâor simply as a meeting place or recreational or social exchangeâwhen not as part of the commercial contracts themselves, the use of computer programs in the formation and performance of the contract, legal transactions on data, or the use of artificial intelligence in contracting.From a legal perspective, the question is whether the CISG, which is a traditional instrument of contract law, is sufficient to respond to the problems posed by the digital economy, specifically in the rise of the so-called SmartCcontracts, and the use of Artificial Intelligence (AI) in the formation of the contract.
Decentralized and Connected Autonomous Vehicle (CAV) networks offer promising advances in safety, efficiency, and real-time decision-making. However, they face significant challenges in authentication, privacy, and scalabilityâespecially in the face of quantum adversaries. This paper proposes a novel post-quantum secure framework that integrates lattice-based Zero-Knowledge Proofs (ZKPs), optimized Binius proofs, and Multi-Layer Compressed Counting Bloom Filters (ML-CCBF) to enable privacy-preserving authentication and model verification in decentralized CAV environments. Our lattice-based ZKP scheme achieves cryptographic commitments in under 5ÎŒs, while Binius proofs verify model integrity in less than 0.17 seconds per update. ML-CCBF ensures scalable membership filtering with 0% false positives across 1000 nodes. Experimental results confirm 100% ZKP soundness, strong resilience against simulated quantum and adaptive attacks, and stable latency under increasing network load. These findings demonstrate that our framework delivers quantum-resilient security, real-time efficiency, and robust scalability, offering a viable solution for trustworthy decentralized intelligence in next-generation vehicular systems.
Type systems are routinely employed in many modern programming languages to statically ensure various notions of runtime safety. We explore issues of typability and notions of safety in two different fields: Firstly, we focus on process calculi with composite channel names, where the type of a channel must somehow be derived from the types of its constituents. This collection of results includes a simple type system for the -calculus, along with some results of expressivity w.r.t. the -calculus; a generic type system for the Higher-Order Κ-calculus, extending a similar type system for the âfirst-orderâ Κ-calculus; and a simple type system for , which aims to highlight a connexion to type structures from class-based/object-oriented languages. Secondly, we focus on the language TinySol, which models core features of the smart-contract language Solidity. Smart contracts are immutable programs with publicly visible code, that run atop a blockchain and are used to manage financial assets of users. Guided by insights from our work in process calculi, we develop type systems for ensuring three different properties: non-interference, call-integrity, and absence of out-of-gas exceptions. Lastly, we seek to tackle some of the shortcomings of the conventional, syntactic approach to type soundness, which had become evident in our previous developments. In particular, we study a peculiar construct in Solidity, known as the fallback function, which is untypable by syntactic type rules. Hence, we turn to a semantic approach to type soundness which allows type safety to be shown, even in cases where well-typedness cannot be proved by ordinary syntactic type rules. We use this approach to propose a method by which type safety may be recovered, even for contracts containing fallback functions, by allowing the programmer to supply a manual proof of type-safety for untypable pieces of code. This method does not depend on specific features of the fallback function, or even of TinySol or Solidity, and it may therefore also be developed for other smart-contract languages.
Abstract Central banks are increasingly experimenting with frontier technologies, such as Central Bank Digital Currencies (CBDC) and Distributed Ledger Technology (DLT) platforms, alongside advancements in traditional systems like the SWIFT network. While prior research has largely examined technology readiness and regulatory frameworks in isolation, their interactive effects on remittance costs remain underexplored. This study investigates how the Frontier Technology Readiness Index (FTRI) moderates the relationship between remittance costs and the Anti-Money Laundering (AML) Index. The lack of integration of these dimensions is a critical research gap that is addressed to help achieve Sustainable Development Goal 10c (SDG-10c) and G20 targets. Panel data regression, followed by fixed and random effects to test robustness, was employed using datasets for remittance-sending and remittance-receiving countries derived from Worldwide Remittance Price data. In remittance-sending countries, enhanced technological readiness combined with a less stringent AML framework is associated with lower remittance costs. On the contrary, despite high technological readiness, in remittance-receiving countries, higher AML stringency tends to increase costs. Technology and AML measures in receiving countries and the amount of remittance paid in sending countries, in isolation, donât have a significant influence on the cost of remittances. The analysis focuses solely on remittance cost efficiency and proposes incorporating transfer speed and transparency in future studies. Findings imply that the prevalence of informal channels like hawala and current pricing models does not encourage and reward higher remittance volume. Remittance cost reduction policies should focus on anti-money laundering measures and technology readiness in conjunction rather than in isolation. Incorporating national indexes provides a clear direction for improving the defined set of variables that are measurable and thus actionable to policymakers.
Nidhi Singh, Usama Awan, Sarah Basahel, Rsha Alghafes
This study addresses a gap in the current research by investigating the relationship between BC based financial solutions and SC recoverability and financial resilience. Previous research provides little empirical evidence on how and under what conditions Fintech improves the manufacturing firm's financial resilience. This empirical research draws on the resource base view (RBV) to investigate the role of Fintech as a driver of better relationship transparency and SC production risk management for financial resilience. The data was collected from 295 engineering manufacturers in India. A key contribution of this study is that it provides new insights by highlighting the role of Blockchain Technology (BCT), built on the Ethereum-based system, in strengthening SC recoverability and enhancing relationship transparency. We present a research framework grounded in the Resource-Based View (RBV) that illustrates how blockchain technology (BCT) can provide firms with critical competencies for developing relationship transparency and managing production risks, thereby enhancing financial resilience in the SC. Relationship transparency, essential for SC recoverability, is pivotal in establishing the link between BCT and SC recoverability. Our findings advise SC managers that relationship transparency improves SC recoverability and may be an important source of financial resilience.
We analyze the vulnerability of decentralized autonomous organizations (DAOs) to speculative exploitation via their redemption mechanisms. Studying a game-theoretic model of repeated auctions for governance shares with speculators, we characterize the conditions under which -- in equilibrium -- an exploitative exit is guaranteed to occur, occurs in expectation, or never occurs. We evaluate four redemption mechanisms and extend our model to include atomic exits, time delays, and DAO spending strategies. Our results highlight an inherent tension in DAO design: mechanisms intended to protect members from majority attacks can inadvertently create opportunities for costly speculative exploitation. We highlight governance mechanisms that can be used to prevent speculation.
Ruba Islayem, Ahmad Musamih, Khaled Salah, Raja Jayaraman · 5 authors
Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.
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.
Leonardo Henrique Lima de Pilla, Alketa Peci, Rodrigo de Oliveira Leite
ABSTRACT Corporatization in the public sector entails decentralizing the provision of public goods and services to more autonomous entities, including state-owned enterprises (SOEs). Research indicates that the decision to corporatize is driven, among other factors, by the pursuit of financial sustainability in public organizations. A continuing debate revolves around whether the political ideology of incumbents is linked to the creation of SOEs. However, limited attention has been given to understanding if incumbentsâ ideology shapes SOEsâ financial performance and, hence, financial sustainability. This is concerning because SOEs operate beyond political cycles, facing pressures from ideologically different governments over time. Herein, we investigate whether the incumbentsâ ideologies shape SOEsâ financial performance. We hypothesize that the more right leaning the incumbent, the greater the SOEsâ financial performance. However, given that incumbentsâ decisions are influenced by their political partiesâ behaviors, the effects of ideology may be contingent on these factors. Thus, we investigate whether the association of incumbentsâ ideology with SOEsâ financial performance is weaker when incumbentsâ political parties display non-policy behaviors (e.g., by prioritizing electoral outcomes or office occupation). We analyze a 2019â2022 panel of 317 SOEs controlled by 27 subnational governments in Brazil with both FGLS and instrumental variable regression approaches. The data comprising 1,116 SOE-year observations confirm our hypotheses. Our research contributes to scholarship on the drivers of public organizationsâ financial performance and sheds light on the role of political contingencies, such as incumbentsâ ideology and party predominant behaviors regarding SOEsâ financial performanceâa commonly overlooked gap in current research.
Claire Cropper, Elizabeth Wulbrecht, Patrick Thomson, Aaron Dotson · 8 authors
Despite nationally reported metrics that suggest high levels of water security, approximately 12% of the United States of America (US) population is estimated to be water insecure based on the definition in United Nations (UN) SDG 6.1.1. Approximately half of these households are served by Decentralized Water Systems (DWS). In contrast, most research, engineering, innovation, and government programs are focused on Centralized Water Systems (CWS). In this perspective, we 1) characterize and define DWS using professional language common for CWS; 2) compare the social, economic, and regulatory responsibilities of stakeholders in each system; 3) outline existing strategies to professionalize this sector; and 4) propose research avenues to further professionalize DWS to ensure households served by these systems receive equitable water service. We demonstrate that while DWS share commonalities with CWS, decentralized systems are less formally structured; have more stakeholders between source and tap; and place much of the regulatory, finance, maintenance, and safety responsibilities on individual households. These responsibilities include ensuring water quantity and quality, selecting appropriate treatment technologies, finance of repairs and upgrades, and transporting water. We propose strategies for researchers to aid in codeveloping innovative DWS solutions, ensuring relevant integration and community-driven models that better support decentralized communities.
Abstract - Before blockchain technology became common, ways to check who owned digital assets and how to protect them were easy to alter. This caused big losses. Blockchain is a new development that will greatly affect how we trade data plus money in a connected society - it is still rather new, and academic studies are scarce, but this situation changes fast. For this review, we first collected mostly peer reviewed sources. We also gathered helpful articles from many channels. The articles we chose show three important parts. They show the main subjects people discuss about blockchain technology. They show the principal groups of these subjects. They show how blockchain will develop also how it will affect society and technology. Key Words: Blockchain, NFTs, Ethereum, Tokenization, Digital Assets.
The prosperity of Ethereum has led to a rise in phishing scams. Initially, scammers lured users into transferring or granting tokens to Externally Owned Accounts (EOAs). Now, they have shifted to deploying phishing contracts to deceive users. Specifically, scammers trick victims into either directly transferring tokens to phishing contracts or granting these contracts control over their tokens. Our research reveals that phishing contracts have resulted in significant financial losses for users. While several studies have explored cybercrime on Ethereum, to the best of our knowledge, the understanding of phishing contracts is still limited. In this paper, we present the first empirical study of phishing contracts on Ethereum. We first build a sample dataset including 790 reported phishing contracts, based on which we uncover the key features of phishing contracts. Then, we propose to collect phishing contracts by identifying suspicious functions from the bytecode and simulating transactions. With this method, we have built the first large-scale phishing contract dataset on Ethereum, comprising 37,654 phishing contracts deployed between December 29, 2022 and January 1, 2025. Based on the above dataset, we collect phishing transactions and then conduct the measurement from the perspectives of victim accounts, phishing contracts, and deployer accounts. Alarmingly, these phishing contracts have launched 211,319 phishing transactions, leading to 190.7 million in losses for 171,984 victim accounts. Moreover, we identify a large-scale phishing group deploying 85.7% of all phishing contracts, and it remains active at present. Our work aims to serve as a valuable reference in combating phishing contracts and protecting users' assets.
We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we introduce a new algorithm based on the FPCA applied by rolling, which can be used for any data observed continuously 24/7. The proposed FPCA forecasting methods are applied to return functions computed from data sampled hourly and at 15-minute intervals. Next, the functional forecasts evaluated at discrete points in time are compared with the forecasts based on other methods, including machine learning and a traditional ARMA model. The proposed FPCA-based methods perform well in terms of forecast accuracy and outperform competitors in terms of directional (sign) of return forecasts at fixed points in time.
Karolina Gorna, Nicolas Iooss, Yannick Seurin, Rida Khatoun
The widespread adoption of the Go programming language in infrastructure backends and blockchain projects has heightened the need for improved security measures. Established techniques such as unit testing, static analysis, and program fuzzing provide foundational protection mechanisms. Although symbolic execution tools have made significant contributions, opportunities remain to address the complexities of Go's runtime and concurrency model. In this work, we present Zorya, a novel methodology leveraging concrete and symbolic (concolic) execution to evaluate Go programs comprehensively. By systematically exploring execution paths to uncover vulnerabilities beyond conventional testing, symbolic execution offers distinct advantages, and coupling it with concrete execution mitigates the path explosion problem. Our solution employs Ghidra's P-Code as an intermediate representation (IR). This implementation detects runtime panics in the TinyGo compiler and supports both generic and custom invariants. Furthermore, P-Code's generic IR nature enables analysis of programs written in other languages such as C. Future enhancements may include intelligent classification of concolic execution logs to identify vulnerability patterns.
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comprehensive evaluation of forecasting methodologies across different market conditions and periods of financial distress. Models' training and hyperparameter tuning procedure is performed using a novel three-fold dynamic cross-validation method. The applicability of applied models is evaluated using both forecast error metrics and trading performance indicators. The obtained findings indicate that the proper construction process of hybrid models plays a crucial role in developing profitable trading strategies, outperforming their individual components and the benchmark Buy&Hold strategy. The most effective hybrid model architecture was achieved by combining the econometric ARIMA model with either SVM or LSTM, under the assumption of a non-additive relationship between the linear and nonlinear components.
Kai Li, Conggai Li, Xin Yuan, Shenghong Li · 11 authors
This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.
This paper presents a comprehensive framework for transaction posting and pricing in Layer 2 (L2) blockchain systems, focusing on challenges stemming from fluctuating Layer 1 (L1) gas fees and the congestion issues within L2 networks. Existing methods have focused on the problem of optimal posting strategies to L1 in isolation, without simultaneously considering the L2 fee mechanism. In contrast, our work offers a unified approach that addresses the complex interplay between transaction queue dynamics, L1 cost variability, and user responses to L2 fees. We contribute by (1) formulating a dynamic model that integrates both posting and pricing strategies, capturing the interplay between L1 gas price fluctuations and L2 queue management, (2) deriving an optimal threshold-based posting policy that guides L2 sequencers in managing transactions based on queue length and current L1 conditions, and (3) establishing theoretical foundations for a dynamic L2 fee mechanism that balances cost recovery with congestion control. We validate our framework through simulations.
Mai Shawkat, Ali El-desoky, Zainab H. Ali, Mofreh Salem
Abstract The Industrial Internet of Things (IIoT) applications have been recognized as an advancement of the conventional wireless network that concentrates on incorporating processes and machines specifically for industrial applications. These Industrial applications frequently use centralized machine learning (ML) approaches not only to enhance their functionality but also to evaluate sensor data for a variety of purposes, including digitizing operations in manufacturers, forecasting maintenance requirements in industrial equipment, and detecting anomalies for security monitoring, they may adversely affect overall system performance due to high cost of computing power and privacy concerns, as so much data is stored on a cloud server. Federated Learning (FL) has emerged as a new benchmark for centralized ML methods. It sends models to user devices without transferring private data to third-party or central servers; it is one of the promising solutions to data leakage issues. This work introduces a comprehensive overview of the advancements, challenges, and future directions in FL adoption with edge devices. It covers security threats and mitigation strategies, emphasizing its categories, privacy and concerns, communication overhead obstacles, heterogeneity issues, aggregation techniques, and associated development tools. This review paper delves into FL-related topics, including system platforms, offering a comprehensive overview of best practice systems in real-world FL applications. To ensure security in IIoT applications, reviewing threats and mitigation strategies by integrating FL with state-of-the-art technologies such as blockchain, federated reinforcement learning, and federated meta-learning has been explored. Finally, the recent research is taking place to determine new future directions and opportunities for FL security defense mechanisms has been considered at the end of this review paper.
ABSTRACT Cryptocurrencies are digital tokens secured by cryptography and recorded on decentralized networks, offering innovations like peer-to-peer finance and token-based fundraising but also posing risks such as wild price swings and laundering opportunities. This study uses doctrinal analysisâreviewing laws (e.g., RBI Act, FEMA, PMLA), court rulings (such as IAMAI v. RBI), and regulatory noticesâto map Indiaâs material and procedural rules for crypto. Indiaâs response has been fragmented: RBI cautions in 2013/17 and a 2018 banking-ban overturned in 2020 left no unified law, while the draft 2021 Bill remains pending. Tax measures in 2022 imposed a 30 % flat tax on crypto gains (Section 115BBH) and 1 % TDS on transfers above âč10,000, but lack clear licensing or consumer safeguards. Comparative review shows the U.S. relies on SEC enforcement (Howey Test) and FinCENâs MSB rules, whereas the EUâs MiCA sets uniform definitions, licensing, and AML/CFT standards. Key gaps in India include unclear asset definitions, no VASP registry, and weak AML âtravel-ruleâ compliance. The dissertation recommends a technology-neutral crypto law with precise definitions, a licensing regime, mandatory KYC, consumer-protection mechanisms, and adaptive sandboxes supported by AI-driven monitoring tools
Software defined networking (SDN) increasingly integrates multiple controllers from diverse vendors to enhance network scalability, flexibility, and reliability. However, such heterogeneous deployments pose significant security threats, especially at the east-west interface which is connecting these controllers. Existing solutions are inadequate for ensuring robust protection across multi-vendor SDN environments as most of them are meant to a specific type of attacks, use centralized solution, or designed for homogeneous SDN environments. This study proposes a blockchain-based security framework to address existing security gaps within heterogeneous SDN environments. The framework establishes a decentralized, robust, and interoperable security layer for distributed SDN controllers. By utilizing the Ethereum blockchain with customized smart contract-based checks, the proposed approach enables mutual authentication among controllers, secures data exchange, and controls network access. The framework effectively mitigates common SDN threats such as distributed denial-of-service (DDoS), man-in-the-middle (MitM), false data injection, and unauthorized access. Experimental results highlight the practicality of the solution, achieving a stable throughput of approximately 20 transactions per second with an average authentication latency of 28-40 ms. These results demonstrate that the proposed framework not only enhances inter-controller communication security but also maintains the network performance, making it a reliable and scalable solution for real-world SDN deployments.
Indiaâs dynamic socio-economic landscape and vast geography necessitate efficient and equitable public service delivery mechanisms, especially in areas like inter-state travel for citizens eligible for complimentary or concessional passes. Traditional systems used for issuing such privilege passes suffer from inefficiencies, fraud vulnerabilities, and inconsistent verification standards across states. This paper proposes a novel smart contract- based authentication mechanism built on a permissioned blockchain architecture to automate and secure the issuance of privilege passes for inter-state travel within India. By integrating Aadhaar-based digital identity verification and leveraging the immutability and transparency of blockchain, this approach enables real-time, verifiable, and tamper-proof pass issuance, while minimizing administrative overhead. The proposed system operates through a consortium-led blockchain network composed of central and state transport authorities and public sector technology partners. Smart contracts deployed on this network enforce eligibility rules automatically by interacting with off-chain data sourcesâreferred to as oraclesâthat provide income, age, or employment status of applicants. Once eligibility is validated, a digital privilege pass is issued, cryptographically linked to the citizenâs identity and stored on-chain. These digital passes can be presented through QR codes and verified instantly by transport officials using mobile applications. Key features of this system include tamper-resistant records, zero-knowledge proof support for privacy preservation, and revocation capabilities for dynamic eligibility management. Aadhaar e-KYC ensures that only legitimate individuals are granted benefits, while the use of hashed identifiers on-chain preserves personal privacy. Furthermore, the permissioned nature of the blockchain network ensures compliance with national data protection laws while allowing rapid consensus and controlled access. By introducing automation, auditability, and interoperability into the travel concession ecosystem, this framework significantly reduces the risk of misuse, streamlines citizen experience, and enhances governance efficiency. The solution supports pan-India applicability and is adaptable to the varied policy rules across different states. Its modular design also enables future expansion to other welfare services, such as subsidized healthcare or education benefits. This paper outlines the technical architecture, smart contract workflow, governance model, and potential challenges in implementing such a system. The integration of decentralized technologies in public service infrastructure marks a progressive step toward a transparent, efficient, and citizen-centric Digital India.