Abdulhadi Sahin, Kemal Akkaya, Sukumar Ganapati, A. Selcuk Uluagac
While blockchain technology has the potential to become one of the enablers of distributed applications and autonomous organizations (DAOs) with its decentralized structure, practical implementations reveal the existence of power and resource concentration at various levels. This concentration raises serious security concerns regarding the core consensus mechanisms, which regulate block creation and the interaction between the nodes of blockchain, potentially preventing the realization of the technology’s full potential. To this end, in this paper, we analyze one of the most commonly deployed consensus protocols, namely Proof of Stake (PoS), from a security perspective. We demonstrate how PoS inadvertently encourages the concentration of power, which may cause some users to leave, thereby endangering blockchain security. Specifically, in PoS, nodes with higher stakes have a greater likelihood of being selected as block proposers, leading to power concentration among a small group of users. This situation increases the risk of collusion and compromise of the consensus mechanism. To address these issues, we propose and analyze an alternative incentive mechanism for PoS by utilizing theories on optimal taxation from behavioral economics. Our proposed model bases the block proposer selection process on a modified and relatively more balanced distribution. It offers preferential treatment to low-stake holders while also integrating a reputation mechanism to mitigate potential Sybil attacks encouraged by equitable rewards. Using a game-theoretic model, we demonstrate the optimal balance of incentives between earning potential and blockchain network sustainability for both high- and low-stake holders. We also conducted extensive performance analyses, including real data from Uniswap, which showed that our proposed mechanism can mitigate inequality and provide an appealing solution for keeping the users in the network to sustain the security of the consensus mechanisms and thus the secure and sustainable operation of blockchain applications.
Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation, blockchain-based auditability, and quantum-secure communication. Each client trains a local hybrid model in which a variational quantum circuit is embedded between classical neural layers, while model updates are protected through homomorphic encryption, threshold secret sharing, and QKD-based keying. A permissioned blockchain records aggregation events and supports reputation-weighted trust among participants. We evaluate QuantumChain on financial transaction data using a compact, size-matched classical baseline to isolate the effect of the quantum layer. Results show that the HQNN achieves comparable accuracy while improving fraud-class recall in most settings, reaching 94.6% recall compared with 93.2% for the classical model. The Deep QLayer improves performance in full-data settings, suggesting that added circuit depth helps recover representational capacity when the shallow circuit becomes limited. Mixed-state simulations further show that the recall trend persists under non-ideal quantum evolution. In federated deployment with 10 heterogeneous clients, global accuracy increases from 97.7% to 98.8% over five rounds before stabilizing. These results show that QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence.
Maximal extractable value (MEV) arises when privileged participants select, exclude, insert, or reorder pending transactions for private gain. We specify and analyze the Themis Consensus Extension v1, first published by Mangata in 2021. The design separates value extraction by reordering (VER) from value extraction by denial (VED). For VER, block construction and execution occur across consecutive producers: one producer commits a transaction set, and the next derives a publicly verifiable, deterministic, previously un- predictable seed and executes a seed-determined, dependency-preserving permutation. For selective VED, a user may encrypt a transaction for a designated builder and executor. The builder removes an outer layer and commits the opaque inner ciphertext; the executor reveals and executes the plaintext only after commitment. Under selfish but non-colluding validators, an adversary below the underlying consensus fault threshold, secure cryptography, and accountable role performance, the construction limits unilateral post-commit ordering control and hides transaction intent from relays and the builder. It does not provide send-order or receive-order fairness, complete censorship resistance, resistance to builder-executor collusion, or per-transaction price guarantees. We analyze probabilistic extraction, spam, dependent transactions, decryption liveness, session boundaries, total denial, and threshold coalitions. We also document the initial Aura-based Substrate implementation and its subsequent transition to a BABE-based sr25519/VRF seed path, together with delayed execution, Fisher-Yates shuffling, and Xoshiro256++. The result preserves the original proposal while narrowing its claims to explicit assumptions.
To meet the throughput demands of modern blockchain systems, protocols for State Machine Replication (SMR) increasingly have many processors disseminate blocks of transactions in parallel, with consensus then establishing a total ordering on the blocks of all producers. Such designs face a choice as to when a block may enter the ordering. Certified approaches wait for a quorum to attest a block's availability, which is robust but adds message delays to every transaction. Uncertified approaches let proposals reference blocks immediately, which is fast but degrades rapidly when referenced data must be fetched on the critical path. Raptr, the state of the art, takes a middle course, finalising the longest prefix of the leader's proposal that a quorum holds, so that no processor ever blocks or fetches. The remaining weakness is sensitivity to order: if the data behind a single early batch is withheld, the proposal finalises little or nothing, so individual faulty producers can still deny the system its optimistic path. We present Multimmit, a protocol for $n \ge 5f+1$ processors combining a consensus layer requiring one round of voting per view with multi-chain data dissemination. Votes are cast relative to the leader's proposal, reporting per chain how far the voter can support it, and may themselves attest fresh blocks beyond it. A transaction block disseminated at time $t$ is ordered by $t+3δ$ in expectation and $t+2δ$ at best, measured from the block's dissemination rather than the leader's proposal. Degradation under faults is graceful: a faulty producer delays only its own chain's blocks, costing other chains at most a one-view wait for placement. No leader can both finalise its leader block and exclude a fresh, well-circulated block of an honest chain. Consensus traffic is tens of kilobytes per view, independent of transaction volume.
Christian Cachin, David Lehnherr, Juan Villacis, François-Xavier Wicht
Sender untraceability hides the account spent by a cryptocurrency transfer among a set of candidates, its masking set. What a transfer does to that set separates two designs: classical schemes retain the whole set and append a nullifier marking the spent account, so the ledger grows with every transfer; constant-state schemes instead consume and replace the entire set. We ask how this choice affects synchronization. We formalize the two designs as the linear and constant untraceable asset transfer objects (LUAT and CUAT) and locate them in the consensus hierarchy. In LUAT, transfers from distinct accounts commute. Its consensus number is 2, compared with 1 for standard asset transfer, independently of the masking-set size and of the untraceability notion, and LUAT is starvation-free. Partitioning the accounts into fixed masking sets lets exhausted sets be garbage-collected without increasing that number. In CUAT, a transfer consumes and replaces every account of its masking set, so two transfers whose sets intersect cannot both take effect. We formalize this with the conflict graph on masking sets, whose edges join sets sharing an account. Under weak untraceability, which protects a transaction in isolation, the consensus number is unbounded already for one-round protocols. Under strong untraceability, which protects against an observer of the complete history, untraceability holds on a history exactly when any two accounts sharing a masking set occur in the same number of the masking sets in it. This uniform incidence bounds the conflict graph, and matching constructions attain it, so the consensus number is determined exactly and grows quadratically in the masking-set size. Finally, CUAT is not starvation-free. The two objects therefore pay for the same privacy differently: LUAT in storage, CUAT in synchronization and fairness.
Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority, but do not inherently provide cryptographic evidence that a concrete request issued by a specific agent satisfies the applicable policy in a specific execution context. This paper hypothesizes that agent authorization can be formalized as a cryptographically verifiable relation, denoted $R_{CVA}$, that jointly binds an agent principal, a concrete authorization request, an execution context, and the satisfaction of an applicable policy, while selectively preserving the confidentiality of private authorization attributes. We introduce a preliminary formal abstraction for Cryptographically Verifiable Agent Authorization (CVA), define a compact set of candidate security properties including authorization soundness, principal binding, request binding, policy binding, and replay resistance, and provide an executable zero-knowledge proof of concept that instantiates selected elements of the model over a Groth16 zk-SNARK construction. We further identify and formalize the structural separation among identity binding, authorization-request binding, and runtime execution binding as a central open problem in the design of secure agentic systems (a distinction {not explicitly addressed by} current agentic security frameworks) and present a falsifiable research agenda for its resolution.
Abstract Sybil attacks are a significant challenge in permissionless blockchains. However, existing research pays limited attention to how different modes of network evolution affect Sybil resistance. In practice, the method of node admission can greatly influence a blockchain’s resilience to Sybil attacks. To address this, we study Sybil resilience using a dynamic network growth model based on preferential attachment and adopt the Identity-Augmented Proof-of-Stake (IdAPoS) protocol as our consensus backbone. First, we extend IdAPoS with an on-chain Sybil-detection mechanism, reducing reliance on off-chain honesty assumptions. Subsequently, we formalize the network expansion procedure in IdAPoS by distinguishing the Applicant-based and Participant-based Network Expansion Models and assessing Sybil resistance under each model. Finally, using agent-based modeling, we simulate voting token value dynamics under Sybil attacks to quantify how expansion strategies affect Sybil resistance. Experiments show that our proposed trustworthiness-evaluation mechanism removes IdAPoS’s reliance on off-chain honesty information by extracting node-level Sybil-suspicion scores from on-chain voting relationships. Sybil attacks in blockchains cannot be eliminated but can only be delayed. Greater centralization among honest nodes generally strengthens Sybil resistance. Under superlinear network growth, the Participant-based Network Expansion Model achieves more stable scaling than the Applicant-based Network Expansion Model. Overall, IdAPoS improves system-level Sybil resilience at the cost of more centralized voting power.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production in modern blockchains is increasingly shaped by economic gains that arise from control over transaction ordering. These gains—known as Maximal Extractable Value (MEV)—have led to concerns about centralization and market power among blockchain consensus participants. To address these concerns, Ethereum introduced Proposer-Builder Separation (PBS), in which specialized block builders compete in block building auctions to construct blocks on behalf of validators. The current implementation of PBS, MEV-Boost, mediates this competition through an open-bid first-price ascending auction, termed the MEV-Boost auction. This paper analyzes the strategic incentives of builders in MEV-Boost auctions. We develop an agent-based simulation framework and apply empirical game-theoretic analysis to study how asymmetries in network latency and access to MEV opportunities shape bidding behavior and market concentration. Our findings show that while latency differences mildly affect builder incentives, MEV opportunity access fundamentally alters equilibrium strategies: builders with privileged access to MEV opportunities bid less aggressively, maintain higher profit margins, dominate market share, and reduce proposer revenue. These effects contribute to centralization and oligopolistic outcomes in the builder market. To validate these findings, we further analyze an idealized symmetric benchmark market where builders have comparable latency and MEV access. Under such settings, the auction behaves as expected—bidding is competitive, proposer revenue is higher, and the market is more decentralized—confirming that the observed inefficiencies arise specifically from the asymmetries present in practice.
Distributed Ledger Technologies (DLTs) have turned out to be an underlying enabler of trust, security, and automation in the next-generation wireless networks (6G). Contrasting centralized control models, the DLTs offer decentralized coordination, record keeping which is immutable, and programmable logic, which is consistent with the ultra-dense and intelligent heterogeneous ecosystems of 6G. The paper has discussed the performance implications of incorporation of the SDLTs with 6G networks in blockchain, directed acyclic graph based ledger and hybrid DLT architectures. There was an integrated DLT-6G framework where cross-layer communication between radio access, core, edge computing, and distributed ledgers was highlighted. To model the latency of transactions, their throughput, energy usage, and consensus overhead were modeled based on the 6G communication characteristics including ultra-low latency, massive connectivity, and edge intelligence. A large-scale set of simulations was done to test the DLT-based network slicing, secure resource orchestration, and AI-assisted ledger management and compared the results to that of traditional non-DLT methods. The results have shown that lightweight and DAG-based DLTs were much more cost-effective in terms of confirmation delay and energy usage, whereas in dense 6G operation, hybrid designs were more scalable and dependable. Moreover, ledger management with the help of AI improved flexibility in changing the conditions of traffic and mobility.
Web3 represents a paradigm shift of the internet from “platform centralization” to “protocol decentralization,” with its core value lying in rebuilding the trust foundation and value distribution logic of the digital world through blockchain technology. In 2026, the global Web3 infrastructure market is projected to reach $9.74 billion, the Web3 social platform market is expanding at a CAGR of 52.1%, and the decentralized physical infrastructure network (DePIN) market is estimated to reach $85 billion. However, the governance dilemma of Decentralized Autonomous Organizations (DAOs)——token voting leading to power concentration, frequent governance attacks, and low decision-making efficiency——is exposing the deep paradox of “code is law.” DeFi total value locked fell from $115 billion in January 2026 to $70 billion in June, a 39% decline; 121 security incidents caused $942 million in losses, reflecting the systemic fragility of decentralized finance. Meanwhile, global regulatory frameworks are accelerating——the EU MiCA is fully applicable, the U.S. GENIUS Act has taken effect, and Hong Kong‘s Stablecoin Ordinance has been implemented——marking Web3’s transition from “regulatory vacuum” to the “compliance era.” Based on the eight-dimensional framework of Prim-Lex Theory——Prim-Unity·Prim-Fire (Web3 network energy metabolic efficiency), Two Principles·Yin-Yang (dynamic balance between decentralization and effective governance), Three Realms·GC⁴A (cross-scale integration from technological infrastructure to application ecosystems to global governance), Four Phenomena·Four Colors (four-phase differentiated strategies for the Web3 lifecycle), Five Elements·Five Models (coupling of computational deficit/phase-transition early warning/spatial zoning/crypto balance sheet/systemic resilience), Six Dimensions·Six Directions (six-directional spatial layout of global Web3 infrastructure and the digital divide), Seven Luminaires·Seven Rhythms (synchronization between technological iteration cycles and governance response cycles), and Eight Trigrams·Eight Information (Web3 information network entropy and on-chain governance transparency)——this paper constructs, for the first time, an eight-dimensional quantitative assessment system for Web3 governance. It elaborates, dimension by dimension, the calculation principles, mathematical formulas, parameter meanings, and data sources. Using three empirical anchors——DAO governance attacks and reform experiments, DeFi protocol risk evolution, and the formation of global regulatory frameworks——this paper demonstrates the application pathway of the eight-dimensional framework in identifying “phase differences” and “critical windows” in Web3 governance, and proposes the “Web3 Governance Health Index” (Ψ_Web3), providing a quantifiable, programmable, and auditable mathematical language and governance tool for the transition of global Web3 from a “decentralized utopia” to an “eight-dimensional coherent state.”
Bruno Ramos-Cruz, Javier Andreu-Perez, David Richerby, Luis Martínez
The operation of blockchain is governed by consensus algorithms (CA). Several consensus mechanisms require significant computational power, while others necessitate high amounts of stakes to select the participant to validate and verify the transactions in the block, leading to centralisation of power and participant exclusion. This paper proposes a novel methodology to address these issues in reputation-based consensus algorithms by studying the reputation behaviour of the validator using intuitionistic fuzzy sets (IFSs) and uninorm aggregation operations (UAOs). Our approach uses IFSs to express the "reputation" because the reputation values in a consensus algorithm eventually imply uncertainty, and IFSs facilitate the representation of a lack of precise knowledge about reputation. Moreover, this methodology utilises uninorm aggregation operations to monitor reputation over time and reinforces the importance of negative and positive reputation. Consequently, this solution allows validators to rectify past failures in subsequent verification processes and foster an equitable consensus algorithm design. The proposed framework maintains linear computational complexity and does not introduce additional communication overhead beyond the underlying consensus protocol. Supported by experimental results, our methodology demonstrates improved performance and evaluation, promising advancements in blockchain network fairness and inclusivity.
K.E. Otebaliyeva, Zh. T. Shaimukhanova, Z. A. Erzhanova, A. .K. Adibayeva
A smart contract is more than a technical phenomenon; it raises legal questions about intent, transaction form, and obligation performance in a digital environment. Kazakhstani law, including the Civil Code and the Law on Electronic Documents, provides a basis for digital tools in contracts, recognizing electronic forms and the principles of freedom of contract. AIFC law further validates automated systems. However, the lack of a conceptual definition in civil legislation creates challenges for public law. This article argues that smart contracts should not be viewed as standalone contract types but through a functional approach: as either a form of expressing intent or an automated performance mechanism. Special focus is placed on criminal proceedings. The authors demonstrate that the absence of a clear definition complicates distinguishing civil torts from cybercrimes and hinders the use of code as evidence or the seizure of digital assets. The core issue is the discrepancy between traditional civil law constructs, blockchain logic, and current procedural evidentiary standards in Kazakhstan.
The pseudonymous nature of blockchain transactions, combined with the rise of encrypted DNS protocols such as DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT), has created a new frontier for sophisticated tax evasion. Malicious actors can now exfiltrate transaction details and coordinate transfers by encoding data within the payloads of encrypted DNS queries, effectively bypassing traditional network monitoring and forensic analysis. This paper proposes a novel detection framework that leverages a hybrid deep learning architecture to identify such covert, tax-evading activities. Our system integrates a Convolutional Neural Network (CNN) for its superior ability to extract spatial and sequential patterns from raw network flow data and encrypted payload characteristics, with a Long Short-Term Memory (LSTM) network to model the temporal dynamics of blockchain interactions and DNS query sequences. By fusing these two paradigms, the hybrid model can distinguish between benign encrypted DNS traffic and malicious payloads used for illicit financial coordination. We evaluate our framework using a synthetically generated dataset that simulates realistic tax-evasion strategies, including micro-transaction splitting and delayed transaction relaying. Preliminary results indicate that our approach achieves a significantly higher detection rate and lower false-positive rate compared to conventional signature-based or single-model machine learning methods. This research demonstrates the efficacy of hybrid neural networks in preserving financial integrity and provides a critical tool for regulatory agencies to enforce tax compliance in the age of encrypted communications and decentralized finance.
Abstract We study comovement among major cryptocurrencies from a portfolio management perspective. To this end, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation conditional on the portfolio return being below or above a given threshold. Second, we develop a new multivariate model named the Common Autoregressive Jump Intensity Score-based (ComARJIS) model in which the time-varying intensity of a common jump in cryptocurrency returns is formulated under the Generalized Autoregressive Score (GAS) framework. Our main findings are as follows: First, we find an adverse downside correlation: the downside correlation is higher than the upside correlation. Second, the ComARJIS model successfully shows the correlation asymmetry of cryptocurrencies. Third, and most importantly, a market-timing strategy with the common jump intensity improves the Sharpe ratio. This result suggests that time diversification could be helpful for cryptocurrency investors even if asset diversification is impossible. Fourth, the meltdown risk represented by the common jump intensity is associated with the financial market stress in the U.S.
Madi Gali, Aray Kassenkhan, Y. Chinibayev, A. M. Abshukirova · 5 authors
Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments.
The convergence of agricultural digitalization and decentralized finance presents critical opportunities for mitigating carbon-related financial risks in emerging markets. However, the integrity of environmental, social, and governance reporting is frequently undermined by information asymmetries and inadequate audit trust. This paper introduces a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, blockchain-verified carbon disclosure pipeline. By deploying distributed Python middleware integrated with serverless computational nodes, the system programmatically extracts agricultural carbon intensity metrics and cross-references them against immutable blockchain ledgers. This methodology structurally eliminates manual reporting friction, providing rural credit institutions and multinational enterprises with deterministic, verifiable environmental data. Preliminary architectural evaluations confirm that integrating high-velocity Application Programming Interfaces with decentralized ledgers significantly reduces information asymmetry, establishing a highly scalable foundation for green finance and rural revitalization.
The digital transformation of commerce has introduced smart contracts—self-executing, code-based agreements on blockchain infrastructure—that challenge traditional legal doctrines by automating performance and minimizing intermediaries. While gaining global traction across finance and supply chains, their legal status within Indian commercial law remains doctrinally fragmented. This article critically examines whether existing Indian statutory frameworks, particularly the Indian Contract Act, 1872, and the Information Technology Act, 2000, can accommodate these blockchain-enabled arrangements. It interrogates foundational legal questions surrounding automated offer and acceptance, algorithmic consent, the enforceability of self-executing obligations, and the evidentiary admissibility of decentralized records. Furthermore, the study analyzes the technological anatomy of smart contracts, distinguishing between legal contracts expressed in code and autonomous software protocols, to highlight legal complexities that conventional doctrines fail to address. Through a comparative examination of regulatory developments in the US, UK, Singapore, EU, and UAE, the article highlights an international movement toward the formal recognition of blockchain contracting. Against this backdrop, it argues that India’s existing legal framework, though interpretively flexible, is structurally inadequate for resolving the jurisdictional, liability, and consumer protection challenges posed by algorithmic transactions. The study concludes that smart contracts necessitate a paradigm shift in commercial jurisprudence. India must move beyond mere interpretive accommodation and develop a coherent legislative architecture that balances technological innovation with legal certainty, contractual fairness, and commercial accountability. Keywords: Smart Contracts; Indian Contract Law; Blockchain Technology; Algorithmic Transactions; Commercial Jurisprudence; Digital Contracts.
The rapid advancement of artificial intelligence, particularly the breakthroughs in large language models and AI agents, is driving a fundamental paradigm shift in the fintech sector. This paper proposes a theoretical framework to characterize the transition of fintech from a "tool empowerment" phase, where technology serves as an efficiency-enhancing instrument within existing financial structures, to an "ecological reconstruction" phase, where AI agents, embedded finance, and decentralized technologies fundamentally reshape the organizational forms, value creation mechanisms, and competitive dynamics of the financial industry. We develop a three-dimensional analytical framework encompassing technological architecture, institutional logic, and value network to systematically examine this transformation. Through a mixed-methods approach combining comparative case studies of 12 representative financial institutions and quantitative analysis of patent data from 2015 to 2025, we find that: (1) the paradigm shift follows a non-linear S-curve trajectory, with a critical inflection point occurring around 2023-2024; (2) AI agent-driven autonomous workflows can reduce operational costs by 35-48% while improving risk assessment accuracy by 22-31%; (3) the ecological reconstruction phase exhibits distinct network effects where platform-based financial ecosystems achieve 2.3-3.7 times higher customer lifetime value compared to traditional linear models; (4) the transition presents significant regulatory challenges, particularly regarding algorithmic accountability, data sovereignty, and systemic risk aggregation in interconnected AI-financial networks. Our findings contribute to the theoretical understanding of technology-induced institutional change in financial systems and offer practical implications for financial institutions, technology firms, and policymakers navigating this transformative period.
One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.
The rapid growth of cybercrime, ransomware attacks, digital fraud, and large-scale cyber threats has significantly increased the need for secure and collaborative cyber forensic investigations. Traditional machine learning approaches often require organizations to share or centralize sensitive forensic datasets, creating challenges related to privacy, confidentiality, data ownership, and security. To address these limitations, this project proposes a PrivacyPreserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption. The proposed framework integrates Federated Learning, Distributed Learning, CKKS-based Homomorphic Encryption, Blockchain Technology, and a Secure Model Exchange Space to enable multiple agencies to collaboratively train machine learning models without exposing their raw forensic data. Federated Learning allows organizations to train models locally and securely aggregate encrypted model updates, while Distributed Learning enables encrypted dataset partitions to be processed collaboratively by helper nodes without revealing the original data. CKKS Homomorphic Encryption protects sensitive information during computation, and blockchain technology provides decentralized trust through secure node authentication, transparent validation, immutable audit trails, and trusted model exchange among participating agencies. The framework is implemented using Python, Flask, Scikit-learn, TenSEAL, Ganache, Solidity, and Web3.py, providing a web-based platform for collaborative project management, encrypted training, blockchain monitoring, secure model sharing, performance evaluation, and cyber forensic prediction. Experimental results demonstrate that the proposed architecture successfully supports secure collaborative learning, encrypted computation, blockchain-based validation, and trusted model sharing while maintaining effective prediction performance. By integrating distributed learning, federated learning, homomorphic encryption, and blockchain into a unified framework, the proposed system provides a scalable, secure, and privacy-preserving solution for next-generation cyber forensic intelligence, enabling organizations to collaboratively strengthen cybersecurity without compromising the privacy, confidentiality, or ownership of sensitive forensic data
Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, ageinvariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations.
Das Forschungs- und Entwicklungsprojekt LCBIT (Low-Code Blockchain Integration Toolkit) wurde mit großem Erfolg durchgeführt. Das übergeordnete Ziel, hochkomplexe Blockchain-Technologien so zu abstrahieren, dass auch Nicht-Softwareentwickler in die Lage versetzt werden, dezentralisierte Anwendungen (dApps) eigenständig zu entwickeln und bereitzustellen, konnte im Berichtszeitraum vollständig erreicht werden. Im Mittelpunkt des Erfolgs stand die methodische und technologische Weiterentwicklung der bestehenden Low-Code/No-Code-Plattform (LCNC) der Heisenware GmbH zu einem umfassenden und intuitiven Werkzeugkasten für Web3-Anwendungen. Durch die exzellente und interdisziplinäre Zusammenarbeit im Konsortium - bestehend aus der Heisenware GmbH, der Hochschule Mittweida (Blockchain Competence Center Mittweida - BCCM), der TU Chemnitz (Professur Fabrikplanung und Intralogistik - FPIL) und dem assoziierten Partner in.hub GmbH - wurde ein modulares System geschaffen. Dieses vereint die Integration von Blockchain, IoT-Sensorik und klassischen Datenquellen nahtlos in einem Low-Code-Umfeld. Das Projekt hat bewiesen, dass sich durch die konsequente Abstraktion technischer Komplexität die Entwicklung dezentraler Anwendungen drastisch vereinfachen lässt. Insbesondere kleine und mittlere Unternehmen (KMU) erhalten dadurch einen niedrigschwelligen Zugang zu Web3-Technologien, ohne kostenintensive, eigene Blockchain-Expertise aufbauen zu müssen.
Decentralized autonomous organizations (DAOs) represent one of the most consequential experiments in organizational design to emerge from blockchain technology. By encoding governance rules into smart contracts and recording every vote, proposal, and treasury decision immutably on-chain, DAOs offer globally distributed communities a high degree of transparency and accountability in collective decision-making. This study examines governance design and participatory innovation across three DAOs: RARI DAO, Arbitrum DAO, and Optimism DAO. Each has taken a distinct structural approach to the problem of collective decision-making at scale. Using a qualitative comparative case study method, the research draws on governance forum discussions, proposal records, and official documentation, analyzed through thematic coding and cross-case comparison. The theoretical frame draws primarily from Ostrom’s (1990) commons governance principles, with Scott’s (1995, 2014) institutional theory and Donaldson’s (2001) contingency theory applied as supplementary analytical lenses. Across all three cases, the findings indicate the emergence of increasingly formalized governance architectures designed to balance decentralization, coordination efficiency, and operational security. Communities building governance infrastructure from scratch, iterating rapidly in response to community feedback, and developing structural solutions: delegate incentive programs, participation incentive mechanisms, bicameral legitimacy systems, constitutional frameworks, and dedicated legal entities that represent an emerging configuration of governance mechanisms. Two cross-case findings are particularly notable. First, all three DAOs independently converged on a three-body governance architecture comprising a legal foundation, a security council, and token-holder governance — suggesting that similar governance problems, encountered in similar technical and legal environments, tend to produce similar structural solutions. Second, while these architectures are structurally similar, they differ significantly in how governance processes are implemented in practice, reflecting differences in scale, formalization, and community context. These findings contribute to the literature by providing a structured cross-case analysis of DAO governance design and offering practical insights into programmable institutional design and blockchain-enabled coordination systems.
Learn more about Theta Network and its impact on the development of decentralized infrastructure via blockchain-enabled media distribution, edge computing, AI integration, and Web3 innovation. With this in-depth overview, you will gain valuable information about its technology, features, practical applications, and future perspectives, emphasizing the need for thorough research before making an investment decision. If you are interested in blockchain, then this article is for you!