L. Bharathi, Kabita Thaoroijam, Sri Raman Kothuri, P Joel Josephson · 6 authors
The blistering development of the Industrial Internet of Things (IIoT) has brought serious issues to the maintenance of large-scale sensor data security and processing with low latency and scalability. Conventional central and edge-only solutions are either limited in the number of trust bottlenecks or restricted in the detection accuracy, thus a hybrid solution is required. This study establishes a Blockchain-AI composite model, where federated anomaly detection and a superior Delegated Proof-of-Stake (eDPoS) consensus mechanism system are used to efficiently and safely process big data on IIoT scenarios. This methodology gives the analytical models that are vital in throughput, latency and the likelihood of hostile takeover. Researcher experimented the Indian IIoT and Blockchain Synthetic Dataset which includes DPoS information under a wide range of conditions, including safe and malicious adversarial stake attacks. It was found to significantly (up to 20 percent) improve throughput over vanilla DPoS, but latency is minimized under medium-delay networks and can anomaly detect (AUC [?] 0.93) with errors nearly equal to centralized (under 5 percent) baselines. Security analysis provides resistance to stake-boost attacks and optimization of storage using lightweight anchoring. This paper makes the framework a scalable and secure IIoT deployment solution, between blockchain consensus and AI-driven anomaly detection.
Blockchain and Federated Learning (FL) provide a strong framework for distributed, privacypreserving machine learning in the medical field. In order to provide safe and effective model training, this framework assists in handling sensitive patient data from lung disease diagnosis, such as CT scans, X-rays, and clinical records. The proposed approach improves distributed machine learning security, privacy, and integrity, particularly in delicate fields like healthcare. Contributions from other datasets help the model get better, but patient data is kept private and blockchain guarantees the integrity of the updates to the model. ZeroKnowledge Proofs (ZKP) guarantee that customers can demonstrate the accuracy of their model upgrades without disclosing any personal information. FLBC- ZKP uses cryptographic proofs to remove this requirement for confidence. FLBC-ZKP models exhibit competitive accuracy rates in healthcare applications, guaranteeing confidentiality and privacy without compromising predictive performance. Contri- butions from other datasets improve the model, but patient information is kept confidential and the blockchain ensures the accuracy of model updates. Compared to regular FL, FLBC-ZKP delivers superior privacy and security through blockchain and ZKP, making it particularly suitable for sensitive healthcare data, while maintaining high accuracy. The accuracy data throughout federated learning rounds for a different approach, FLBC-ZKP slightly surpasses the other methods as the number of rounds increases.
The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network interoperability. We propose the Decentralized Interstellar Agent Protocol (DIAP), a novel framework for agent identity and communication that enables persistent, verifiable, and trustless interoperability in fully decentralized environments. DIAP binds an agent's identity to an immutable IPFS or IPNS content identifier and uses zero-knowledge proofs (ZKP) to dynamically and statelessly prove ownership, removing the need for record updates. We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange. DIAP introduces a zero-dependency ZKP deployment model through a universal proof manager and compile-time build script that embeds a precompiled Noir circuit, eliminating the need for external ZKP toolchains. This enables instant, verifiable, and privacy-preserving identity proofs. This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.
Zero-knowledge proof is one of the most promising privacy-preserving approaches in the current literature. However, its complex nature leads its development and deployment to be time-consuming and error-prone. Furthermore, despite the increasing adoption of zero-knowledge proof in the blockchain applications, there is still no systematic framework that streamlines the end-to-end life-cycle of proof development. This paper introduces the first LLM-enhanced zero-knowledge proof DevOps framework for blockchain (i.e. zkOps) to the literature. To evaluate the performance of the framework on different real-life scenarios, a small benchmarking suite is constructed with the increasing computational complexity with respect to the size of circuit constraints. The experimental study identifies the effects of the model temperature on the code compilation rate, and the complexity of prompts on the service latencies. The findings show that the framework efficiently handles the varying-complexity of prompts with a maximum successful compilation rate of 70% (i.e., up to 200,000 proof constraints).
The authors presented a blockchain-backed cloud artificial intelligence (AI) system for smart contract risk assessment in decentralized finance (DeFi) that replaces the coarse-grained structure-awareness of graph neural networks (GNNs) with fine-grained structure-awareness while preserving contextually-rich interactions between nodes that are similar to transformer encoders. The program is able to identify a wide range of vulnerabilities and assign unambiguous risk rankings to smart contracts. People can be educated about decentralized finance without sacrificing their privacy through the use of a method called “federated learning.” This is just a single component of the job. The platform is constructed on top of a robust and adaptable cloud platform that utilizes on-chain risk evaluations that are anchored to ensure that they are able to be audited. The system has been proven to be accurate, fast, and simple to relocate based on a large number of simulations and real-world measurements of prohibited traffic behaviors, delay analysis, and economic consequences. The research also makes official the automatic and reliable risk analysis in decentralized finance, which makes the DeFi ecosystems significantly safer and more open.
The rapid progress of quantum computing poses significant challenges to traditional cryptographic mechanisms, necessitating the adoption of post-quantum cryptography (PQC) solutions. This paper proposes a Quantum-Enhanced Security for Smart Meters (QESM) system to protect power plant data in smart cities, integrating Kyber for secure key exchange, FALCON (Fast-Fourier Transform over Lattice-based Cryptography) for quantum-resistant digital signatures, and ZKP (Zero-Knowledge Proof) for effective verification without revealing sensitive data to secure power plant data against quantum attacks. To evaluate the security of the proposed system, we analyze its resistance to various quantum threats, including Shor’s algorithm, Grover’s algorithm, quantum key analysis, quantum reversal encryption, quantum amplification, quantum switching, and quantum collision attacks. In the current study, accurate measures were used and the average was approximately 7.065 (bits/byte) for randomness, the average execution time was 6.202 milliseconds, the average memory consumption was approximately 4.343 KB, 6.4 Completeness was equal to 1 and unforgeability was 100%. As for the average throughput, it was approximately 485,605 operations per second. That shows the QESM system provides strong security and efficiency, making it a viable solution for protecting the electricity infrastructure in smart cities in the quantum era.
David Davó, Javier Arroyo, Samer Hassan, Silvia Semenzin
Despite the hype and scandals around blockchain, there are valuable applications beyond finance, such as decentralized autonomous organizations (DAOs). DAOs are self-governed online communities where users vote and manage budgets transparently. In under a decade, DAOs have evolved from theory to managing billions of dollars. Blockchain enthusiasts launched DAO platforms like our case study, “DAOstack”, promising large-scale collaboration and quickly securing millions in funding. Today, we can critically evaluate to what extent the platform followed up on its promises. In this work, we analyze DAOstack using a mixed-methods approach combining quantitative and qualitative data. In particular, we quantitatively examined its 92 organizations in terms of size, lifespan, activity, power concentration, and the effectiveness of its governance model. We also interviewed in-depth 6 DAOstack core users to delve deep into their experiences using the platform. Our analysis shows that DAOstack mainly hosted small, short-lived DAOs, with some exceptions. Its governance model was functional, but the economic incentives underpinning it were ineffective. The analysis of the interviews reveals interesting aspects such as the power imbalances due to token ownership and reputation, and that the voting system, though innovative, was affected by issues of cost and complexity. We conclude by discussing the challenges these platforms face and advocating for a multidisciplinary experimental approach for future DAO designers.
The constant evolution of Decentralized Finance (DeFi) calls for the continuous monitoring of its developments and implications through a critical review of the academic literature. While DeFi holds promise for enhancing economic activity by expanding market access for enterprises and promoting financial inclusion, concerns remain that digital assets are primarily used for speculative purposes rather than for financing the real economy. This study employs bibliometric methods to investigate whether and how the current academic literature addresses the potential influence of DeFi on real economic dynamics. Employing bibliometric methods—including co-citation, bibliographic coupling, and keyword co-occurrence analyses—focused on DeFi-related publications in the Economics and Business subject areas within the Scopus database, the study maps the knowledge base, author networks, and thematic trends and their temporal evolution, supporting regulators, researchers, and practitioners. The findings reveal that the integration of DeFi with the real economy has received limited attention in scholarly research. This highlights the need for further investigation into DeFi’s implications for financial stability, productive investment, and long-term economic growth.
The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$.
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.
The oracle problem refers to the inability of an agent to know if the information coming from an oracle is authentic and unbiased. In ancient times, philosophers and historians debated on how to evaluate, increase, and secure the reliability of oracle predictions, particularly those from Delphi, which pertained to matters of state. Today, we refer to data carriers for automatic machines as oracles, but establishing a secure channel between these oracles and the real world still represents a challenge. Despite numerous efforts, this problem remains mostly unsolved, and the recent advent of blockchain oracles has added a layer of complexity because of the decentralization of blockchains. This paper conceptually connects Delphic and modern blockchain oracles, developing a comparative framework. Leveraging blockchain oracle taxonomy, lexical analysis is also performed on 167 Delphic queries to shed light on the relationship between oracle answer quality and question type. The presented framework aims first at revealing commonalities between classical and computational oracles and then at enriching the oracle analysis within each field. This study contributes to the computer science literature by proposing strategies to improve the reliability of blockchain oracles based on insights from Delphi and to classical literature by introducing a framework that can also be applied to interpret and classify other ancient oracular mechanisms.
Vehicular ad-hoc networks (VANETs), considered a pivotal component of intelligent transportation systems (ITS), are susceptible to both established and emerging security vulnerabilities. However, existing authenticated key management schemes fail to provide effective conditional anonymity during decentralized authentication process. Meanwhile, scalable and reliable vehicular pseudonym management is absent, resulting in potential privacy leakage. Furthermore, conventional group key agreement schemes inherently fail to properly accommodate the highly dynamic topological characteristics of vehicular environments, which significantly limits their practical applicability. To address these challenges, the blockchain-assisted anonymous authentication and tree-based group key agreement design is proposed in this paper. Firstly, the pairing-free decentralized authentication mechanism is designed to enable mutual authentication between vehicles and roadside units (RSUs). Secondly, the threshold-varying pseudonym management system is designed, leveraging secret sharing and smart contracts to ensure conditional privacy preservation. This mechanism utilizes the multi-RSU consensus to recover the user's real identity, enabling traceability of malicious entities. Thirdly, the self-balancing tree-based group key agreement mechanism is proposed, optimizing key generation efficiency in dynamic vehicular environments. Crucial security requirements can be satisfied via the security analysis, whereas the performance evaluation substantiates the superiority of the proposed scheme over existing approaches.
Pradeep Nazareth, Sathyaprakash T, Sharan S Shetty, Shrihith S Poojary
Internet of Things (IoT) devices are constrained by limited storage and processing capabilities, creating open entry points for cyber threats. These constraints limits the establishment of robust security using conventional, centralized methods. Blockchain technology presents a promising solution by offering a decentralized, secure, and tamper-proof method for storing data. This paper examines modern research directions for implementing blockchain to enhance the security and quality of IoT systems. The core strength of blockchain lies in its ability to protect information from corruption and unauthorized access through encrypted, distributed ledgers. However, a significant challenge remains where many standard blockchain implementations are computationally expensive and demand high processing power, making them unsuitable for lightweight IoT devices. Therefore, the primary issue is not the applicability of blockchain’s security principles to IoT, but rather the prohibitive cost and resource requirements for many practical use cases. This research focuses on overcoming these barriers to enable efficient, featherweight blockchain solutions for the IoT landscape.
The global transition to sustainable energy is critical for achieving development goals and addressing climate change. Beyond technology, economic paradigm shifts are now seen as essential to reshaping power markets and accelerating this transition. This paper explores key economic transformations driving the shift, focusing on innovative financial mechanism such as green bonds, blended finance and de-risking tools that mobilize capital for clean energy. It also examines the role of carbon pricing instruments like Emissions Trading Systems in incentivizing decarbonization and the rise of decentralized business models, including Virtual Power Plants and Energy-asa-Service and their implications for market structures and revenue streams. Drawing from international case studies and a review of current literature, the study identifies effective policy frameworks and financing strategies. Applying these insights to Vietnam’s energy context, it offers targeted recommendations to attract investment, reform power market design and strengthen the green finance ecosystem. This research contributes to a deeper understanding of the economic levers vital for enabling a sustainable, inclusive and resilient power sector aligned with Vietnam’s net-zero goals.
As blockchain technology deepens its integration into various fields, the challenges of blockchain as an isolated distributed ledger are becoming increasingly prominent. Effectively breaking the isolation of various blockchain ledgers and enabling information flow and value transfer between blockchains is a key area of current blockchain research. Hash timelocks are a key technology for achieving this cross-chain nature, offering advantages such as decentralization, ease of implementation, and high cross-chain efficiency. However, the hash timelock mechanism still suffers from issues such as the inability to consistently match cross-chain transaction partners and the inability to transfer assets. This severely impacts the user experience and hinders the usability of hash timelocks in cross-chain transactions. Therefore, building on existing hash timelock technology, this paper proposes a mechanism that utilizes an intermediate user pool to enable instant cross-chain asset transfers. This mechanism also incorporates a dynamic transaction matching algorithm to achieve efficient transaction matching. This mechanism not only enables decentralized cross-chain asset transfers, but also eliminates the indeterminate waiting time required for matching by traditional hash timelock mechanisms. Finally, experiments demonstrate the feasibility of this new mechanism, demonstrating significant efficiency advantages for small and medium-sized cross-chain transactions, eliminating the need for additional transaction matching time.
The Riemann Hypothesis (RH) has remained one of the most significant unsolved problems in mathematics for over 160 years. This paper posits a novel argument that the resistance of the RH to proof stems not from mathematical intractability, but from a fundamental ontological incompatibility. The hypothesis, we argue, implicitly presupposes a Platonic ontology, wherein infinite sets (such as the set of all non-trivial zeros) exist as complete, static objects accessible to timeless logical inspection. As a counter-framework, we introduce the KnoWellian Universe Theory (KUT), a procedural ontology where mathematical facts do not pre-exist but are continuously rendered into actuality. KUT is founded upon the Axiom of Bounded Infinity (-c > ∞ < c+), which rejects the hierarchy of completed infinities, and operates via a ternary time structure (Past, Instant, Future) that governs the dynamic interplay of Control (actualized reality) and Chaos (unmanifested potential). From these axioms, we derive the Law of KnoWellian Conservation (a(t) + w(t) = N), which formally partitions reality into a finite set of rendered facts, a(t), and a vast, unrendered potential, w(t). We demonstrate that a deductive proof of the RH would require certain knowledge of the properties of the unrendered set w(t), a logical impossibility for any observer existing within the procedural universe. Through the 'Bernharda' thought experiment, we illustrate that any consciousness capable of such a proof would necessarily be a 'Boltzmann Brain'—a mind predicated on the ontologically false Platonic substrate. We conclude that the Riemann Hypothesis is not provably true or false within a KnoWellian framework, but is un-renderable: a beautiful and well-formed question formulated in the language of static 'being' that cannot be answered in a universe of dynamic 'becoming'. The paper includes a formal proof of un-renderability, a discussion of objections and implications, and a comparison between Platonic and KnoWellian (procedural) ontologies, positioning KUT within the historical context of foundational debates in mathematics (e.g., Intuitionism).
Alexy Bounsavath, Csaba Kiss, Tamás Savci, Gábor Hellner · 5 authors
The exponential growth of blockchain-based tokens has heightened the need for reliable methods to assess their longterm viability at deployment, a stage where historical market data is absent and risks such as scams and project failures are prevalent. This study introduces an explainable machine learning framework to predict token viability using static features available at launch, including smart contract properties (e.g., mintability, centralization), deployment details (e.g., network), and metadata (e.g., presence of an icon). We collected 100,000 ERC-20 tokens from Ethereum, Binance Smart Chain, and Polygon and analyzed their characteristics available at deployment and derived features. We labeled them as live or failed based on post-deployment scores derived from liquidity, transfer frequency, and holder distribution. Among the models evaluated, XGBoost with class-weight adjustment excelled, creating an enriched token set that contained, on average, 11 times more live tokens than the original dataset, surpassing other classification models in identifying viable tokens. SHAP analysis highlighted key predictors: tokens with icons, complex yet high-quality code, and deployment on Ethereum were more likely to succeed, while Polygon deployments correlated with higher risk. Though effective as an early filter, the framework's modest standalone precision underscores its role as part of a broader strategy integrating post-launch data. This approach advances early-stage token evaluation, enhancing investor decision-making and risk assessment in decentralized finance.
Abstract In today’s era of digital transformation, online transactions have become vital to financial systems, e-commerce, and decentralized applications. However, increasing dependence on digital payment infrastructures has also raised major security concerns such as hacking, identity theft, and unauthorized access. To address these challenges, the proposed project “Blockchain Secure Transaction” presents a decentralized framework that ensures transparency, integrity, and confidentiality in digital transactions. The system uses blockchain technology to record and validate each transaction in a distributed ledger, eliminating centralized control and making data immutable and tamper-proof. The workflow begins with user registration, where users provide details and set a picture password for secure recognition. During login, the system verifies credentials and performs biometric authentication to confirm user identity. Unregistered users are redirected to the registration page, maintaining process integrity. Once authenticated, users access the dashboard to initiate secure transactions. To preserve privacy, Zero-Knowledge Proof (ZKP) is used, allowing users to prove transaction authenticity without revealing sensitive information. Transactions then pass through smart contract verification, which ensures compliance with predefined conditions. Successful verifications result in completed transactions, while suspicious or invalid ones are blocked or frozen automatically. All user data and transaction logs are securely stored in Firebase, with backend processing handled in Java and the frontend designed using React (app.jsx). By combining blockchain’s immutability, smart contract automation, ZKP privacy proofs, and biometric authentication, the Blockchain Secure Transaction System offers a multi-layered, tamper-resistant, and transparent solution for secure online payments — enhancing trust and reliability in the digital economy.