The Fischlin transform yields non-interactive zero-knowledge proofs with straight-line extractability in the classical random oracle model. This is done by forcing a prover to generate multiple accepting transcripts through a proof-of-work mechanism. Whether the Fischlin transform is straight-line extractable against quantum adversaries has remained open due to the difficulty of reasoning about the likelihood of query transcripts in the quantum-accessible random oracle model (QROM), even when using the compressed oracle methodology. In this work, we prove that the Fischlin transform remains straight-line extractable in the QROM, via an extractor based on the compressed oracle. This establishes the post-quantum security of the Fischlin transform, providing a post-quantum straight-line extractable NIZK alternative to Pass' transform with smaller proof size. Our techniques include tail bounds for sums of independent random variables and for martingales as well as symmetrization, query amplitude and quantum union bound arguments.
Arka Pal, Louai Zahran, William Gvozdjak, Akilesh Potti · 5 authors
As large language models (LLMs) continue to grow in size, fewer users are able to host and run models locally. This has led to increased use of third-party hosting services. However, in this setting, there is a lack of guarantees on the computation performed by the inference provider. For example, a dishonest provider may replace an expensive large model with a cheaper-to-run weaker model and return the results from the weaker model to the user. Existing tools to verify inference typically rely on methods from cryptography such as zero-knowledge proofs (ZKPs), but these add significant computational overhead, and remain infeasible for use for large models. In this work, we develop a new insight -- that given a method for performing private LLM inference, one can obtain forms of verified inference at marginal extra cost. Specifically, we propose two new protocols which leverage privacy-preserving LLM inference in order to provide guarantees over the inference that was carried out. Our approaches are cheap, requiring the addition of a few extra tokens of computation, and have little to no downstream impact. As the fastest privacy-preserving inference methods are typically faster than ZK methods, the proposed protocols also improve verification runtime. Our work provides novel insights into the connections between privacy and verifiability in LLM inference.
This research demonstrates the environmental impacts of Digital Currencies (DC), particularly focusing on Bitcoin's (BTC) energy-intensive Proof-of-Work (PoW) process as well as a fundamental expectation for sustainable alternatives, which can be termed as Green Coins (GC) which are expected to be able to maintain the benefits of BTC, while generating little to no negative impacts on the environment. Bitcoin is estimated to consume about 150 TWh annually, a measure comparable to that of a mid-sized country, while also generating 60 to 90 million metric tons of$\text{CO}_{2}$emissions and about 30,000 metric tons of electronic waste (e-waste) through deliberate accelerated hardware obsolescence. On the other hand, GC tends to use more efficient proofs such as Proof of Stake (PoS) and Proof of Space-time (PoST) and examples include Ethereum following its 'Merge' estimated a reduction of over 99 % of energy use and Dogecoin has and even lower environmental impact compared to BTC. Using data sets from the Cambridge Bitcoin Electricity Consumption Index (CBECI) and Digiconomist, this study quantifies Bitcoin's carbon footprint and tracks the trends from 2017 to 2025, through more extensively investigating its sustainability profile relative to its GC counterparts. Findings reveal Bitcoin continues to have high energy use and e-waste, peaking in 2021, while both Ethereum (ETH) and Dogecoin (DG) had significant gains in sustainability improvements. Addressing scaling, security, and regulatory issues, the paper highlights the potential of sustainable financing within the digital financial markets to drive Green technologies, which is increasingly important for aligning cryptocurrency financing with Environmental, Social, and Governance (ESG) parameters, providing a way to continue to innovate while decarbonizing digital financing.
Subject of the study. Decentralized data management models and their impact on the economic performance, business processes, and innovation capacity of enterprises. The aim of the study. Scientific substantiation of the mechanisms and methodological approaches for assessing the economic impact of decentralized data management on enterprise operations, resource allocation, and strategic decision-making. Research methods. The study is based on methods of systemic and comparative analysis, structural-logical modeling, synthesis of open-source financial and operational data, and the development of integrated analytical frameworks for measuring transaction efficiency, process performance, and value creation. Results of the work. The article examines decentralized data governance frameworks based on blockchain, distributed ledger technology (DLT), Web3 platforms, edge computing, and AI-driven smart contracts. It has been substantiated that these technologies form a transparent, secure, and resilient information environment, reducing transaction costs, accelerating business processes, and enabling algorithmic trust. The research demonstrates that decentralized models transform enterprise economic activity by optimizing operational expenditures, enhancing transparency, and creating new models of governance and financing. The study provides an integrated system of indicators for evaluating economic effects, including metrics for transaction cost reduction, process efficiency, digital asset capitalization, and the establishment of digital trust. The research also identifies critical challenges and risks associated with the implementation of decentralized models, including technological integration, regulatory compliance, data privacy, and organizational readiness, which must be addressed to maximize economic and operational benefits. Practical significance. The results provide a methodological and analytical foundation for enterprises seeking to implement decentralized data management solutions to enhance operational efficiency, strengthen innovation capacity, and achieve long-term competitiveness. The study offers guidance for policymakers, managers, and researchers in designing, implementing, and evaluating advanced decentralized governance systems.
The exponential rise of blockchain technology is changing the way organizations operate, including in enforcement. Standard means of conflict resolution whether through courts or arbitration/mediation bodies, regularly face questions of cost, delay, jurisdiction and transparency. One of the innovations in an online and global market can be the blockchain-based decentralized judicial systems, as a result of such limitations. Family law disputes as a case study for the indepth analysis of DDR Blockchain-based Decentralized Dispute Resolution (DDR) systems, and how they could disrupt justice delivery processes in future. Both such models allow people to work out their differences without or so much help from a central authority. They accomplish this using decentralized governance, distributed ledgers, cryptographic security, and smart contracts. Disputes are settled using transparent rules, automated policing and community-based judgment. Critical topics are touched upon simultaneously: the legality of the system, pitfalls of bad governance, challenges to its scalability, voting biases in token systems and ethical dilemmas raised by machine decision-making. Decentralized methods of justice, the study suggests, are unlikely to replace courts as we know them in the near future. Rather, they're promising as secondary solutions - especially in the Web3 world, for digital assets, online commerce, and cross-border transactions. Blockchain dispute resolution can revolutionize the industry of justice in a digital era. It takes away intermediaries on the way to good legal systems through technology. This will enhance the legitimacy, effectiveness and accessibility of dispute resolution for all stakeholders.
In democratic systems, secure and transparent voting mechanisms are essential to maintain public trust and electoral integrity. Traditional paper-based and centralized electronic voting systems often face challenges such as limited transparency, risk of data manipulation, and dependence on centralized authorities. To address these issues, this project proposes a decentralized blockchain-based voting system designed to enhance security, transparency, and reliability. The system is developed on the Ethereum blockchain, where each vote is recorded as an immutable transaction to prevent tampering or duplication. Smart contracts written in Solidity automate essential election functions including voter registration, vote validation, and result computation. A web-based interface built using React.js and Web3.js enables secure interaction with the blockchain, while wallet-based authentication ensures that each authorized user can cast only one vote The system is implemented and tested in a controlled environment to evaluate performance, accuracy, and resistance to double voting.
Global illicit fund flows exceed an estimated $3.1 trillion annually, with stablecoins emerging as a preferred laundering medium due to their liquidity. While decentralized protocols increasingly adopt zero-knowledge proofs to obfuscate transaction graphs, centralized stablecoins remain critical transparent choke points for compliance. Leveraging this persistent visibility, this study analyzes an Ethereum dataset to establish an empirical baseline for behavioral AML detection. Our findings demonstrate that domain-informed tree ensemble models achieve higher Macro-F1 score, significantly outperforming graph neural networks, which struggle with the increasing fragmentation of transaction networks. The model's interpretability goes beyond binary detection, successfully dissecting distinct typologies: it differentiates the complex, high-velocity dispersion of cybercrime syndicates from the constrained, static footprints left by sanctioned entities. This methodological approach provides actionable insights that align with industry shifts toward deterministic verification, informing the auditability and compliance requirements under regulations such as the EU's MiCA and the U.S. GENIUS Act while minimizing unjustified asset freezes. By providing a high-precision behavioral classification of suspicious wallets, this approach contributes to raising the economic cost of financial misconduct while informing compliance practice under emerging stablecoin regulations.
Private agents do not internalize the impact of their investment decisions on the sovereign’s bond prices and default risk. Therefore, a standard externality argument implies that investment is insufficient and that a subsidy can improve welfare, if financed by non-distortionary means. We contrast this logic with a countervailing force. When the sovereign is impatient relative to households, plausibly due to political economy factors, it finds laissez-faire capital accumulation excessive and might prefer instead to tax it. We embed both mechanisms in a sovereign default model with decentralized capital investment, long-term public debt, and stochastic trend growth, calibrated to salient features of the Spanish economy. We find that the impatience channel dominates quantitatively, to such an extent that laissez-faire is preferable to the government’s ideal fiscal policy, based on households’ welfare.
Smart contracts, primarily written in Solidity, are Turing-complete programs on platforms like Ethereum, requiring gas fees for deployment and execution. Gas quantifies computational costs, and inefficient contracts result in unnecessary expenses for developers and users. Gas optimization at the source code level has been studied in various related works; however, existing methods for summarizing gas-inefficient patterns primarily rely on author-defined rules or heuristic approaches, and their evaluations lack a labeled dataset.In this paper, we conduct a comprehensive empirical study on the issue of gas optimization in smart contracts. We begin by gathering audit reports from Code4rena, a well-known smart contract audit platform. These reports include both expert evaluations, conducted by professionals known as Wardens, and automated analyses generated by the platform’s static analysis tool, 4naly3er. After filtering out false-positive gas optimization instances from the automated reports, we identify 2,095 instances of gas-inefficient patterns across 54 projects. We categorize these inefficiencies into 24 types using thematic analysis and find that static analysis tools often produce false positives and negatives. To address this, we propose a hybrid method combining static analysis and large language models (LLMs) to detect and repair gas inefficiencies. The static analysis tool identifies potential optimization opportunities, while the LLM refines these findings and suggests effective repairs. Our evaluation shows that our approach achieves a precision rate of 82.28% and a recall rate of 88.46%, and can save 919 units of gas per function on average during execution.
This study proposes the multi-layered Decentralised Autonomous Identity System (DAIS) for safe, scalable, and policy-aware identity management for autonomous AI agents. DAIS uses distributed ledgers, trust registries, cryptographic credential processing, and adaptive governance principles to authenticate and authorise without central authority. For security and operational reliability, the architecture uses efficient verification techniques, Byzantine fault-tolerant consensus, hierarchical credential state management, and behaviour-aware trust scoring. The mathematical definitions of credential validation, revocation propagation, and consensus guarantees show how DAIS accomplishes constant-time verification and quick revocation dissemination over distributed systems. A 50-agent testbed, PBFT-based registry duplicates, and enterprise-grade hardware were used to evaluate DAIS to OAuth 2.0 and OpenID Connect under identical settings. DAIS regularly outperforms centralised solutions in latency, revocation speed, computational overhead, interoperability, and scalability, improving verification time by fourfold and revocation responsiveness by sixteen-fold. These findings show that DAIS is a durable, high-performance, and future-ready identification infrastructure for large autonomous agent ecosystems.
Najaf Iqbal, Muhammad Abubakr Naeem, Hang Luo, Walid Bakry
Using 5-minute data of 16 cryptocurrency tokens belonging to 5 different categories (AI, Gaming, Meme, Layer 1/2, and FAN tokens), we investigate the risk transmission in higher moments, i.e. realized volatility (RV), realized skewness (RS), and realized kurtosis (RK), employing the TVP-VAR framework and robustness tests. We also perform six sub-sample investigations on various geopolitical and other systemic events. Ethereum, Binance Coin, and Ripple are strongly related to other tokens. Sandbox, Decentraland, and Enjin Coin lead spillover transmission, while Numeraire, Measurable Data Token, and Cryptex Finance absorb most of the shocks. The connections are stronger regarding RV than RS and RK, showing potential for tail-risk reduction, which is heterogeneous regarding extreme events. AI tokens are the least connected during normal conditions as well as most of the extreme events, except the US presidential Election, which puts these tokens in the centre of the system. The Israel-Palestine war, the FTX collapse, and the SEC approval of the first Bitcoin ETF are among the most important events regarding enhancement in the higher-moment risk transmission. Token market investors/traders and regulators can draw essential insights from our findings.
Introduction In the contemporary digital health landscape, securing personal health data against unauthorized access while ensuring its verifiability is a paramount challenge. A critical conflict exists between the transparency required for data verification and the privacy mandated by global regulations such as HIPAA and GDPR. Existing Layer-1 blockchain solutions suffer from prohibitive gas costs and high latency, rendering them unsuitable for real-time monitoring of high-volume health data streams. Methods This paper proposes TeleZK-L2, a novel framework that synergizes distributed Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) with Layer-2 scaling solutions. The architecture introduces a Distributed Prover Network (DPN) to parallelize heavy cryptographic computations and utilizes Optimistic Proof Aggregation to minimize on-chain data footprints. The verification logic is anchored on the Polygon zkEVM to ensure high throughput and low-cost settlement. Results Extensive simulations on a 16-node high-performance cluster demonstrate that TeleZK-L2 generates proofs at a rate 40% faster than the standard Groth16 baseline. Furthermore, the framework reduces on-chain verification costs by approximately 52%. The system maintains constant-time verification complexity regardless of batch size, achieving a peak throughput of 260 TPS. Discussion TeleZK-L2 provides the technical privacy guarantees necessary to support adherence to HIPAA and GDPR data minimization mandates while maintaining cryptographic soundness. By resolving the "Scalability-Privacy Trilemma," this framework demonstrates significant potential for large-scale deployment in national telehealth infrastructures and remote patient monitoring ecosystems.
The growing popularity, the exponentially expanding market size, and the volatility of Cryptocurrency are gaining the attention of all, whether it is investors, policymakers, miners, or academicians. So, this paper has used Bibliometric analysis to explore the existing works of literature in the area of Business, Finance, and Economics. We have reviewed and analysed 1344 articles extracted from the Web of Science core collection, Clarivate Analytics of the period from 2011 to mid-2022 using VOSviewer and Biblioshiny (Biblimetrix: R package) analytical tools. This paper has presented citations, publications, and the impact of sources, documents, authors, organizations, countries, etc., along with their relationships with the help of tables, charts, and network diagrams. The analysis shows exponential growth in the last 4-5 years. Bitcoin and Cryptocurrency (or Cryptocurrencies) are the most frequent keywords. With many ups and downs, cryptocurrency is maintaining its pace with a gradual increase in its acceptability worldwide.
The first machine-checked formalization, in any proof assistant, of any component of the Birch-Swinnerton-Dyer (BSD) conjecture pipeline. No prior Lean, Coq, or Isabelle project has formalized Silverman height bounds, Gross-Zagier-Kolyvagin data structures, or the logical architecture of BSD generator search. No new number theory is proved. The algorithms formalized are the engines inside Cremona's mwrank and SageMath. The contribution is the formalization itself and the foundational analysis it enables. Three results that did not previously exist in formal mathematics: (1) A machine-checked axiom/theorem boundary for BSD. The formalization identifies exactly which ingredients must be axiomatized (Gross-Zagier, Kolyvagin, Silverman bound, positive-definiteness) and which can be proved constructively (height bound chain, finite grid membership, search space finiteness). This is the blueprint for any future formally verified BSD solver: the deep analytic theorems interface with type theory through a single chokepoint (the real-valued Silverman bound), and everything below that chokepoint is verified. (2) A logical characterization of the Archimedean/p-adic dichotomy. The positive-definite Archimedean metric (u = 1) is identified as the exact logical modality lowering search complexity from Pi^0_1 (unbounded, MP) to Delta_0 (bounded verification, BISH). This foundational statement does not appear in the classical literature -- Cremona and Watkins use height bounds as engineering, not as a theorem in reverse mathematics. (3) A logical explanation of the exceptional zero pathology. The p-adic BSD exceptional zero (Mazur-Tate-Teitelbaum) is usually explained analytically: trivial zeros of p-adic L-functions, extra Euler factors. This formalization gives a logical explanation: the p-adic canonical height is not positive-definite, so the MP-to-BISH conversion fails. The search remains unbounded because the metric lacks the topological property needed for logical reduction. This re-reading of a classical analytic obstruction as a failure of logical reducibility is, to our knowledge, new. The axiom budget is minimal -- removing any one ingredient breaks the proof chain -- characterizing the necessary logical interface between analytic number theory and formal verification. This is the first application of constructive reverse mathematics to a Clay Millennium Problem. Formalized in Lean 4 + Mathlib with zero sorry's and zero custom axiom declarations. All analytic axioms enter as Prop-valued hypotheses in a BSDRankOneData structure. Axiom audit: every theorem depends only on [propext, Classical.choice, Quot.sound] (standard Mathlib infrastructure for the reals). Package contains compiled PDF (10 pages), LaTeX source, and complete Lean 4 source (7 files, ~725 lines) buildable with lake build.
Four ancient civilizations — Egyptian, Sumerian, Babylonian, and Hindu — independently preserved the same mathematical encoding system. When their 'mythological' timescales are divided by 144, they reveal real historical intervals, astronomical cycles, and geological periods with precision that cannot be coincidental. Statistical significance: P < 10⁻⁴⁸ (less than one chance in a number with 48 zeros). THE DISCOVERY: Ancient 'mythical' chronologies are not fiction. They are real timescales multiplied by 144 — a universal encoding system designed to preserve historical data across civilizational collapse. To decode ancient mythology into history: divide by 144. When we do this systematically across four independent cultures, the results are stunning. EGYPTIAN EVIDENCE (Turin Papyrus): Pre-dynastic 'mythical' periods when decoded: Total: 36,620 years ÷ 144 = 254.3 years Matches: Biblical Flood to Egyptian Dynasty 1 (254 years) Precision: 110 days (0.12% error) Pre-Shemsu Hor: 23,200 years ÷ 144 = 161.1 years Matches: Flood to Tower of Babel dispersion (161 years) Precision: 37 days (0.06% error) Shemsu Hor: 13,420 years ÷ 144 = 93.2 years Matches: Babel to Nile Valley settlement (93 years) Precision: 73 days (0.2% error) Egyptian P-value: < 10⁻⁸ (less than one in 300 million) The Egyptian scribes encoded the exact chronology from the Flood to their civilization's founding — 254 real years disguised as 36,620 'mythical' years. SUMERIAN EVIDENCE (King List): All eight pre-flood king reigns are EXACT multiples of 144 with zero error: King Reign (years) ÷ 144 Result Alulim 28,800 200.00 Perfect Alalgar 36,000 250.00 Perfect En-men-lu-ana 43,200 300.00 Perfect En-men-gal-ana 28,800 200.00 Perfect Dumuzid 36,000 250.00 Perfect En-sipad-zid-ana 28,800 200.00 Perfect En-men-dur-ana 64,800 450.00 Perfect Ubara-Tutu 36,000 250.00 Perfect 8 out of 8 = 100% exact multiples. Total pre-flood period: 302,400 years = 144 × 2,100 = 14,400 × 21 The Sumerians encoded exactly 21 complete geomagnetic excursion cycles (each 14,400 years) before the catastrophic 22nd cycle that became "The Flood." Sumerian P-value: < 10⁻¹⁶ (less than one in ten quadrillion) King Alulim's reign (28,800 years = 144 × 200) represents one complete Great Year — Earth's true harmonic precessional cycle before the Younger Dryas impact perturbed it to the current 25,772 years. BABYLONIAN EVIDENCE (Berossus): All ten pre-flood kings (Berossus, c. 290 BCE) divide perfectly by 144: King Reign (years) ÷ 144 Result Aloros 36,000 250 Perfect Integer Alaparos 10,800 75 Perfect Integer Amelon 46,800 325 Perfect Integer Ammenon 43,200 300 Perfect Integer Megalaros 64,800 450 Perfect Integer Daonos 36,000 250 Perfect Integer Euedorachos 64,800 450 Perfect Integer Amempsinos 36,000 250 Perfect Integer Otiartes 28,800 200 Perfect Integer Xisuthros 64,800 450 Perfect Integer 10 out of 10 = 100% exact multiples. Total antediluvian period: 432,000 years Divided by 144: 432,000 ÷ 144 = 3,000 (exact) Babylonian P-value: < 2 × 10⁻²⁴ (less than one in 2 septillion) King Otiartes (9th king) reigned for 28,800 years — identical to Sumerian King Alulim. Both cultures encoded the Great Year in the same king's reign. HINDU EVIDENCE (The Smoking Gun): Hindu Kali Yuga duration: 432,000 years Babylonian total: 432,000 years EXACT MATCH. Both = 144 × 3,000 precisely. Testing all four Hindu Yugas: Yuga Duration ÷ 144 Result Kali Yuga 432,000 3,000 Perfect Dwapara Yuga 864,000 6,000 Perfect Treta Yuga 1,296,000 9,000 Perfect Satya Yuga 1,728,000 12,000 Perfect 4 out of 4 Yugas = 100% exact multiples of 144. The k-values (3,000, 6,000, 9,000, 12,000) form a perfect 1:2:3:4 ratio. Hindu P-value: < 10⁻⁸ Babylon-Hindu match P-value: < 10⁻¹⁶ (the probability they'd both preserve 432,000 independently by chance) THE BABYLONIAN-HINDU BRIDGE: This is the smoking gun. Babylon (Mesopotamia) and Hindu civilization (India) are separated by: 2,500 miles of geography Completely different languages, religions, mythologies Independent cultural development across centuries Yet they both preserve IDENTICAL numbers: 432,000 years = 144 × 3,000 This is not cultural borrowing. This is not coincidence. This is evidence of a common source — a global pre-flood civilization that used 144-based timekeeping, which both Mesopotamia and India inherited independently. COMBINED STATISTICAL ANALYSIS: Testing across all four cultures: Total independent data points: Egyptian: 3 periods Sumerian: 8 kings Babylonian: 10 kings Hindu: 4 Yugas Total: 25 numerical values Probability all 25 would divide by 144 to yield meaningful results by random chance: P < 10⁻⁴⁸ One chance in: 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 For context: Stars in observable universe: ~10²⁴ Atoms in human body: ~10²⁸ This probability: 10⁻⁴⁸ This cannot be coincidence. This is proof. WHAT THE EVIDENCE PROVES: Before the Flood, there existed a global civilization that: Used 144 as the universal temporal constant Same number appears in Egypt, Mesopotamia, India Same encoding method (multiply real years × 144) Same astronomical knowledge (Great Year, excursion cycles) Survived multiple geomagnetic excursion cycles Sumerian record: 21 consecutive 14,400-year cycles Developed protocols for Type 1 reset survival Encoded survival knowledge in mythology Tracked astronomical cycles with precision Great Year: 28,800 years (true harmonic precession) Excursion cycle: 14,400 years (geomagnetic resets) Solar cycle: 144-day beats All based on 144 constant Transmitted knowledge to successor cultures Sumerians: Preserved raw data (king lists) Babylonians: Systematized data (432,000 total) Egyptians: Encoded data (pre-dynastic mythology) Hindus: Preserved data (Yuga cosmology) Built a universal timekeeping system Sar unit: 3,600 years = 144 × 25 Sexagesimal (base-60) mathematics Encoded in mythology to survive collapse THE METHODOLOGY: Simple, reproducible, falsifiable: Take any ancient 'mythological' chronology Divide by 144 Check if result corresponds to: Known historical intervals Astronomical cycles Geological events Other verified 144-based timescales If 144 encoding is universal, it will work across ALL major cultures. If coincidental, it will fail for most. WHY 144? This constant appears across EVERY tested domain: Planetary (spatial): All planet diameters = 144 × Fibonacci(n) miles (P < 10⁻¹⁸) Planetary (temporal): All planet orbits = 14.4-day multiples (P < 10⁻⁵⁰) Solar: Sunspot cycle = 144 days × 28 (exact) Geological: Geomagnetic excursions = 14,400-year intervals Deep time: Permian-Triassic extinction = 14,400 × 17,500 (exact) Human: Earth's day = 1,440 minutes = 144 × 10 Ancient chronology: Egyptian + Sumerian + Babylonian + Hindu = all encode via × 144 (P < 10⁻⁴⁸) This is a fundamental organizing principle operating fractally across nine orders of magnitude in time. WHY ENCODE VIA MULTIPLICATION? Three complementary reasons: Durability: Mythological narratives survive collapse better than administrative records. Epic poems and temple inscriptions are memorized and carved in stone. By encoding real chronology as 'god-king' myths, scribes ensured data survival even if civilization was destroyed. Dual purpose: Encoded numbers serve both religious functions (satisfying ritual requirements) and data storage (preserving actual chronology). One document accomplishes both. Astronomical connection: Using 144 linked human chronology to the same constant governing planetary mechanics and solar cycles. This embedded human history within cosmic time. FALSIFIABLE PREDICTIONS: If the framework is correct: Additional Egyptian sources (Palermo Stone, Abydos, Manetho) will decode via ÷ 144 to known intervals All Hindu chronological units (Manvantaras, Kalpas) will be exact 144 multiples Chinese Bamboo Annals (independent East Asian culture) will show 144 encoding or NOT (critical test) Mayan Long Count units beyond B'ak'tun will all be 144 multiples May 2027 will show measurable precession rate change (if 28,800-year Great Year is true) If ANY major culture with preserved chronologies does NOT show 144 encoding, this requires explanation. IMPLICATIONS: Ancient mythology is not fiction. It is real chronology encrypted with a mathematical key. That key is 144. When applied systematically: Mythology becomes history History becomes precise The past is no longer hidden The gods ruled for 432,000 years. Divide by 144. They ruled for 3,000 years. That is real. That is history. That is how long the pre-flood world lasted. And four ancient cultures — separated by thousands of miles, different languages, independent mythologies — all preserved it exactly. Each one encoding the same truth in their own sacred texts. We now have the decryption key. RELATED PUBLICATIONS: All papers in the CTF framework series available at ctftheory.com and Zenodo with permanent DOIs. Key papers: The 144 Hz Universal Constant Across Space and Time Sumerian Great Year Decoded: All Eight Pre-Flood King Reigns Are Exact Multiples of 144 Two Types of Catastrophe: Harmonic Resets vs. Impact Events Sun 144-Day Beat and 14,400-Year Reset Cycle Planet Nine Primordial Black Hole at the 144 Harmonic Node All data public. All calculations reproducible. All predictions falsifiable.
Stablecoins are becoming more common in the Fin-Tech (Financial Technology) ecosystem as they keep their value stable and combines easily with decentralized finance inherent in the financial technology ecosystem due to their price stability and simplicity of integration in decentralized finance (DeFi), cross-border payments, and automated trading systems. However, the same characteristics that propel utility transaction speed, pseudonymity, and automation through smart contracts have also made them vulnerable to financial manipulation. Tactics such as wash trading, spoofing, and pump-and-dump schemes have become more prevalent, compromising market integrity significantly. However, major technical challenge in detecting these fraudulent activities and behaviors, especially under conditions of extreme class imbalance even the legitimate transactions vastly outnumber fraudulent ones.This paper introduces SC-GAN, a conditional Generative Adversarial Network that addresses the scarcity of fraudulent samples by synthesizing realistic blockchain-based fraud instances. The model conditions on key financial and transactional features native to blockchain systems, enabling the generation of high-fidelity synthetic data. We then compare SC-GAN with traditional oversampling techniques like SMOTE and Borderline-SMOTE on a variety of supervised classification models. Our experiments on a real-world stablecoin transaction dataset show with the help of SC-GAN improves both the F1 Score and overall accuracy that is resulting in more efficient detection of rare but crucial fraudulent transactions. This approach also provides the possibility for stronger fraud prevention methods and risk management policies within FinTech platforms.
Zibin Lin, Taotao Wang, Shengli Zhang, Long Shi · 6 authors
Open Web 3.0 platforms increasingly operate as \emph{service ecosystems} (e.g., DeFi, DAOs, and decentralized social applications) where \emph{admission control} and \emph{account provisioning} must be delivered as an always-on service under bursty demand. Service operators face a fundamental tension: enforcing Sybil resistance (one-person-one-account) while preserving user privacy, yet keeping on-chain verification cost and admission latency predictable at scale. Existing credential-based ZK admission approaches typically require per-request on-chain verification, making the provisioning cost grow with the number of concurrent joiners. We present \textbf{ZK-AMS}, a scalable admission and provisioning layer that bridges real-world \emph{Personhood Credentials} to anonymous on-chain service accounts. ZK-AMS combines (i) zero-knowledge credential validation, (ii) a \emph{permissionless} batch submitter model, and (iii) a decentralized, privacy-preserving folding pipeline that uses Nova-style recursive aggregation together with multi-key homomorphic encryption, enabling batch settlement with \emph{constant} on-chain verification per batch. We implement ZK-AMS end-to-end on an Ethereum testbed and evaluate admission throughput, end-to-end latency, and gas consumption. Results show stable verification cost across batch sizes and substantially improved admission efficiency over non-recursive baselines, providing a practical and cost-predictable admission service for large-scale Web 3.0 communities.
We study the implications of the existence of weak Zero-Knowledge (ZK) protocols for worst-case hard languages. These are protocols that have completeness, soundness, and zero-knowledge errors (denoted $ε_c$, $ε_s$, and $ε_z$, respectively) that might not be negligible. Under the assumption that there are worst-case hard languages in NP, we show the following: 1. If all languages in NP have NIZK proofs or arguments satisfying $ ε_c+ε_s+ ε_z < 1 $, then One-Way Functions (OWFs) exist. This covers all possible non-trivial values for these error rates. It additionally implies that if all languages in NP have such NIZK proofs and $ε_c$ is negligible, then they also have NIZK proofs where all errors are negligible. Previously, these results were known under the more restrictive condition $ ε_c+\sqrt{ε_s}+ε_z < 1 $ [Chakraborty et al., CRYPTO 2025]. 2. If all languages in NP have $k$-round public-coin ZK proofs or arguments satisfying $ ε_c+ε_s+(2k-1).ε_z < 1 $, then OWFs exist. 3. If, for some constant $k$, all languages in NP have $k$-round public-coin ZK proofs or arguments satisfying $ ε_c+ε_s+k.ε_z < 1 $, then infinitely-often OWFs exist.
Nurgul Bakytbekovna Aiupova, Md Tota Miah, Krisztina Taralik
ABSTRACT Blockchain technology has emerged as a potential disruptor in non‐financial reporting practices for firms to publicly report their social and environmental impact with its promise of immutability and decentralization. In this context, this study employs a bibliometric analysis to explore the scientific advancements of blockchain applications in CSR reporting from 2015 to 2025. VOSviewer and Biblioshiny in Rstudio applications were employed to perform the required analysis. Drawing data from Scopus and Web of Science (153 articles), the results reveal a significant shift in focus from traditional corporate social responsibility (CSR) reporting mechanisms toward technology‐enabled sustainability reporting. The thematic analysis presents five significant areas for further exploration, including corporate governance and sustainability strategy, technology‐driven sustainable finance, CSR reporting and credibility, ESG performance and digital innovation, and blockchain for accountability and responsibility. The proposed conceptual framework suggests integration of technology‐organization‐environment (TOE) elements when introducing new technology within the organization. Future researchers can empirically test the framework's antecedents to assess the socio‐economic context of different types of non‐financial reporting.
Eman Alatawi, Anoud Alhawiti, Doaa Albalawi, Umar Albalawi
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments.
Fraud detection within the Ethereum network remains a major research challenge due to the strong statistical resemblance between legitimate and fraudulent transaction patterns, severe class imbalance, and the multiscale complexity of temporal-interaction dependencies. Proposing and evaluating a multi-branch attention-based system with automated architecture optimization, which can detect fraudulent Ethereum accounts with high accuracy, is the aim of this study. The experimental evaluation was performed on a dataset with 9,841 samples and 17 extracted features. The proposed system employed a hybrid multi-branch architecture combining CNN, Bi-LSTM, and LSTM with a Gated Fusion mechanism along with multiscale attention layers. The Artificial Bee Colony (ABC) algorithm was applied to automatically optimize sixteen key structural and learning parameters. The results indicate that the proposed system achieved an accuracy of 99.84 %, F1 score of 98.94 %, sensitivity of 98.76 percent, and precision of 99.12 percent. These results notably outperform eight algorithms, such as Random Forest, XGBoost, LGBM, and GADL. According to the confusion matrix analysis, there is a reduction in false negatives, confirming that the system produced only five such cases in the sample set. These findings show that the proposed system is an effective and efficient approach for detecting fraud in blockchain systems and enables deployment in exchanges, DeFi platforms, and regulatory institutions.