Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna
The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.
Ali Akarma, Toqeer Ali Syed, Salman Jan, Hammad Muneer · 5 authors
The AI-based sensing and autonomous monitoring have become the main components of wildfire early detection, but current systems do not provide adaptive inter-agent coordination, structurally defined human control, and cryptographically verifiable responsibility. Purely autonomous alert dissemination in the context of safety critical disasters poses threats of false alarming, governance failure and lack of trust in the system. This paper provides a blockchain-based governance-conscious agentic AI architecture of trusted wildfire early warning. The monitoring of wildfires is modeled as a constrained partially observable Markov decision process (POMDP) that accounts for the detection latency, false alarms reduction and resource consumption with clear governance constraints. Hierarchical multi-agent coordination means dynamic risk-adaptive reallocation of unmanned aerial vehicles (UAVs). With risk-adaptive policies, a permissioned blockchain layer sets mandatory human-authorization as a state-transition invariant as a smart contract. We build formal assurances such as integrity of alerts, human control, non-repudiation and limited detection latency assumptions of Byzantine fault. Security analysis shows that it is resistant to alert injections, replays, and tampering attacks. High-fidelity simulation environment experimental evaluation of governance enforcement demonstrates that it presents limited operational overhead and decreases false public alerts and maintains adaptive detection performance. This work is a step towards a principled design paradigm of reliable AI systems by incorporating accountability into the agentic control loop of disaster intelligence systems that demand safety in their application.
Ali Akarma, Toqeer Ali Syed, Abdul Khadar Jilani, Salman Jan · 7 authors
Autonomous underwater vehicles (AUVs) and sensor nodes increasingly support decentralized sensing and coordination in the Internet of Underwater Things (IoUT), yet most deployments rely on static trust once authentication is established, leaving long-duration missions vulnerable to compromised or behaviorally deviating agents. In this paper, an interrogator based structure is presented that incorporates the idea of behavioral trust monitoring into underwater multi-agent operation without interfering with autonomy. Privileged interrogator module is a passive communication metadata analyzer that uses a lightweight transformer model to calculate dynamic trust scores, which are used to authorize the forwarding of mission critical data. Suspicious agents cause proportional monitoring and conditional restrictions, which allow fast containment and maintain network continuity. The evidence of trust is stored in a permissioned blockchain consortium which offers identity management which is not tampered and is decentralized without causing the overhead of public consensus mechanisms. Simulation based analysis shows that the evaluation of the result compares to a relative improvement of 21.7% in the detection accuracy compared to the static trust baselines with limited energy overhead. These findings suggest that behavior driven validation has the capability of reinforcing underwater coordination without compromising scalability and deployment.
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process. To instantiate this paradigm, we introduce LOCARD, the first agentic framework for blockchain forensics. LOCARD operationalizes this perspective through a Tri-Core Cognitive Architecture that decouples strategic planning, operational execution, and evaluative validation. Unlike generic LLM-based agents, it incorporates a Structured Belief State mechanism to enforce forensic rigor and guide exploration under explicit state constraints. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. We introduce Thor25, a benchmark dataset comprising over 151k real-world cross-chain forensic records, and evaluate LOCARD on the Group-Transfer Tracing task for dismantling Sybil clusters. Validated against representative laundering sub-flows from the Bybit hack, LOCARD achieves high-fidelity tracing results, providing empirical evidence that modeling blockchain forensics as an autonomous agentic task is both viable and effective. These results establish a concrete foundation for future agentic approaches to large-scale blockchain forensic analysis. Code and dataset are publicly available at https://github.com/xhyumiracle/locard and https://github.com/xhyumiracle/thorchain-crosschain-data.
Blockchain and blockchain-inspired decentralized applications are on the rise thanks to their unique characteristics such as their decentralized nature, anonymity, and tamper-proof nature; however, blockchain transactions tend to experience long end-to-end latency, with a major contributor being the block creation step, which might block transaction processing. There are two approaches to ameliorate this overhead: speeding up the block creation process, or processing transactions before block creation finishes. In this project, we work towards designing a self-adaptive block creation process that automatically selects optimal configurations based on workload and hardware resources by defining mathematical models to predict transaction latency based on design and environmental parameters, developing measurement techniques to collect performance-related metrics in docker-hosted blockchain systems and observing trends to build intuition, and defining a mathematical model to predict transaction success rate under various key accessing patterns and block size configurations, validating it with simulation-based measurements.
Sarisht Wadhwa, Aviv Yaish, Fan Zhang, Kartik Nayak
Modern blockchains increasingly rely on parallel execution to improve throughput. We show several industry and academic transaction fee mechanisms (TFMs) struggle to simultaneously account for execution parallelism while remaining performant and fair. First, if parallelism affects fees, adversarial protocol manipulations that offset possible benefits to throughput by introducing fake transactions become rational: users can insert functionally useless parallel transactions solely to reduce fees, and schedulers can create useless sequential transactions to increase revenue. Execution contingency, a core feature of expressive programming languages, both exacerbates the aforementioned threats and introduces new ones: (1) users may overpay for unused resources, and (2) scheduler revenue is harmed when reserved scheduling slots go unused due to contingency. We introduce a framework for this challenging setting, and prove an impossibility, highlighting an inherent tension: both parallelism and contingency involve a trade-off between minimizing risks for users and schedulers, as favoring one comes at the expense of the other. To complete the picture, we introduce a fee mechanisms and prove that they achieve the boundaries of this trade-off. Our results provide rigorous foundations for evaluating designs advanced by notable blockchains, such as Sui and Monad.
This paper challenges the prevailing paradigm of large language models as the cognitive foundation of autonomous AI systems. Through empirical adversarial testing of Qwen3.6-Plus (April 2026) — the most capable publicly available agentic model at time of writing — we identify and document a fundamental epistemological limit in LLM-based autonomous governance: the observer-embeddedness ceiling. Every failure in the structured test suite shares the same root cause: the model cannot reason about the validity of its own observations when the observer is embedded in the system being observed. This ceiling is structural, consistent, and not addressable by increasing model scale. As an alternative, the paper presents a complete six-layer daemon architecture positioning deterministic process management as the foundation of autonomous intelligence, with LLMs relegated to boundary translation only. The architecture runs on commodity hardware (demonstrated on Apple M1 Max 64GB), operates at near-zero marginal cost per decision cycle (48× cost advantage over LLM-agent frameworks), and produces machine-native structured knowledge that accumulates permanently rather than being re-approximated each session. Key contributions:1. Empirical proof of the observer-embeddedness ceiling through 10 structured adversarial tests with full grading documentation2. Complete six-layer daemon architecture specification with reference implementation (Layers 1–6: process model, state persistence, decision functions, inter-daemon communication, governance, LLM integration)3. Machine-native knowledge architecture with six typed subsystems (State Store, Decision Store, Causal Graph, Contradiction Store, Pattern Store, Verified Truth Store)4. Progressive deployment model scaling from 500GB through 2TB, 10TB, and 96TB storage tiers, each enabling qualitatively distinct system capabilities5. Economic analysis demonstrating 48× cost reduction versus LLM-agent frameworks at operational maturity The theoretical foundation connects to the I=E×O framework and the Civilizational Library of Events (CLoE) concept developed in prior RRC-AI work. The paper argues that genuine machine intelligence emerges not from larger language models but from layered deterministic systems with precise state management, verified knowledge accumulation, and LLM involvement only at the human-language boundary. This work extends: Chalupka, R. (2025). The Theory of Integrated Intelligence. Zenodo. https://doi.org/10.5281/zenodo.17541664 Part of the RRC-AI Research Initiative. License: CC BY-NC-SA 4.0 International.
Local government forms the cornerstone of democratic decentralization and grassroots governance across political systems. This study presents a comparative functional analysis of local government models—namely the Anglo-Saxon, Continental (French), and Indian systems—focusing on key areas such as policing, education, finance, and governance structures. By examining variations in autonomy, administrative control, and fiscal capacity, the paper highlights how historical, constitutional, and political contexts shape the functioning of local institutions. The Anglo-Saxon model emphasizes decentralization and flexibility, the Continental model reflects centralized supervision and uniformity, while the Indian model represents a hybrid framework balancing constitutional recognition with fiscal dependence. The analysis underscores that while local governments are universally recognized as vital instruments of democratic governance, their effectiveness is contingent upon adequate financial empowerment, functional clarity, and institutional capacity. The study contributes to the broader discourse on decentralization, governance efficiency, and democratic deepening in the 21st century. Keywords Local Government; Comparative Politics; Decentralization; Anglo-Saxon Model; Continental Model; Indian Model; Fiscal Autonomy; Governance; Public Administration; Democratic Decentralization
A zero-knowledge proximity proof certifies geometric nearness but carries no commitment to an application context. In stateful geo-content systems, where drops can share coordinates, policies evolve, and content has persistent identity, this gap can permit proof transfer between application objects unless extra operational invariants are maintained. We present a systems-security analysis of this deployment problem: a taxonomy of context-binding vulnerabilities, a formal off-circuit verification model for a transcript-adversary that holds a recorded proof but cannot obtain fresh coordinates, an assumption comparison across five binding strategy classes, and a concrete instantiation, Zairn-ZKP, that embeds drop identity, policy version, and session context as public circuit inputs. Compared with a strong off-circuit alternative based on stored-digest server checking, in-proof binding reduces operational invariants from four to two and adds no measurable proving cost relative to the sound geo-only baseline (-0.12 ms median in our setup). It also removes a correctness pitfall we identify empirically: a plausible off-circuit implementation that omits one server-side check remains vulnerable to cross-drop transfer. Measurements across six network conditions, seven venues in four countries, and an epoch-window simulation indicate that same-epoch transfer is realistic in dense urban deployments unless per-request nonces are maintained. Across five platforms and seven binding strategies, the results support a deployable methodology for reducing assumption surfaces in stateful ZK-backed verification workflows.
Location-based systems that combine encrypted geographic search with zero-knowledge proximity proofs typically treat the two phases as independent. Under an honest-but-curious server, this leaves an authorization provenance gap: once session state is purged, no forensic procedure can attribute a proof to its originating search session, because the proof's public inputs encode no session-identifying information. We formalize this gap as the search-authorized proof (SAP) security notion and show via a concrete audit re-association attack that proof-external mechanisms, where authorization evidence remains outside the proof, cannot prevent forensic misattribution when the same drop parameters recur across sessions. Search-Bound Proximity Proofs (SBPP) realize the SAP requirements without modifying the ZKP circuit: session nonce, Merkle-root result-set commitment, and signed receipt are decomposed into independently auditable components, enabling property-level fault isolation in offline audit. Experiments on synthetic and real-world data (110,776 OpenStreetMap POIs) show sub-millisecond absolute overhead on a 125 ms Groth16 baseline.
Maritime shipping carries over 80% of global trade, yet cold-chain compliance verification forces a choice between disclosing sensitive telemetry and issuing unverifiable declarations. The EU's Digital Product Passport mandate requires verifiable provenance, but maritime IT systems lack a harmonized event model for interoperability. This thesis presents Ocean DPP, integrating EPCIS 2.0, oneM2M, IOTA anchoring, and Groth16 zero-knowledge proofs to verify compliance without revealing sensor data. Merkle-tree batching amortises on-chain cost, and sixteen experiments over 10,000+ events confirm 48 ms baseline latency, sub-10 ms proof verification, 37% scaling improvement, and zero message loss. The results demonstrate that privacy-preserving, standards-compliant DPPs are viable for maritime supply chains.
This Handbook presents Execution Governance as a unified cross-domain discipline for autonomous and execution-bound systems. It is not a collection of previously published white papers, but a foundational corpus that reorganizes and systematizes the author’s Execution Governance research into a single canonical reference framework. The Handbook is aligned with EG 2.0 v1.4 and treats structural admissibility, continuity preservation, commit validation, and constraint invariance during candidate formation as core governance requirements. It distinguishes structural continuity from present legitimacy and extends governance analysis from the execution boundary to the full formation-to-commit path. Organized into Foundations, Structural Architecture, Cross-Domain Applications, and Toward Standardization, the Handbook covers governance semantics, responsibility models, verifiable execution, maturity models, policy language primitives, and domain profiles across autonomous systems, Agentic AI, Physical AI, Smart City, Digital Nation, NetZero & ESG, Elderly Care Technology, Genuine News, Industry 6.0, and Web3. The application chapters are presented as domain profiles of a single governance doctrine rather than separate theories. This work is intended as a scholarly and professional reference for structural governance, pre-standardization discussion, academic curricula, industry adoption, regulatory alignment, and cross-domain execution-bound system design.
Tokenized assets increasingly operate across heterogeneous blockchain networks and off-chain ledgers, where a regulatory action (a freeze, a seizure, a confiscation) must take effect atomically and consistently across every domain holding the asset. We mechanize, in Isabelle/HOL, cross-domain state preservation as a functor: state machines are objects, structure-preserving synchronization maps are morphisms, and the category laws (identity, composition, associativity) hold as theorems. On this base we establish four results. Safety: a regulatory transition on one domain is faithfully reflected across all connected domains, with bidirectional roundtrip preservation, N-domain consistency, per-asset isolation, and terminal states preserved. Liveness: under f = 3f+1 shown to make that assumption inhabitable rather than vacuous. Convergence: from an arbitrary unlocked configuration, with no initial cross-chain consistency assumed, synchronization reaches a valid state in a bounded number of steps along a terminal-faithful recovery path. Hierarchy: a tower of synchronization-degree functors connected by natural transformations closed under composition, with a genuinely one-directional degree monotonicity. We couple the functor to Lochbihler and Maric's authenticated data structure at the global-state level, instantiated on a recursive model of the Canton transaction tree with a declared consensus-scope limit. The synchronization model is atomic; its lift to a partially synchronous network is future work. The application is a regulatory state transition model distilled from the RCP framework (arXiv:2603.29278). All ten Isabelle/HOL theory files build without sorry or oops and are submitted to the Archive of Formal Proofs.
Agentic AI rivals human capabilities across a wide range of domains. Looking ahead, it is foreseeable that AI agents will autonomously handle complex workflows and interactions. Early prototypes of this paradigm are emerging, e.g., OpenClaw and Moltbook, signaling a shift toward Agent-to-Agent (A2A) ecosystems. However, despite these promising blueprints, critical trust and security challenges remain, particularly in scenarios involving financial transactions. Ensuring secure and reliable payment mechanisms between unknown and untrusted agents is crucial to complete a fully functional and trustworthy A2A ecosystem. Although blockchain-based infrastructures provide a natural foundation for this setting, via programmable settlement, transparent accounting, and open interoperability, trust and security challenges have not yet been fully addressed. Hence, for the first time, we systematize blockchain-based A2A payments, e.g., X402, with a four-stage lifecycle: discovery, authorization, execution, and accounting. We categorize representative designs at each stage and identify key challenges, including weak intent binding, misuse under valid authorization, payment-service decoupling, and limited accountability. We highlight future directions for strengthening cross-stage consistency, enabling behavior-aware control, and supporting compositional payment workflows across agents and systems.
Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis.
\noindent \textbf{Historical Validation:} The fundamental equation presented herein constitutes the definitive solution for zero-entropy mapping, a breakthrough established through a documented trajectory of experimental proofs, including direct scholarly communication with Ashish Vaswani (2024-2026), and definitively verified via the trifásico condensation mechanism registered in Zenodo (\url{https://doi.org/10.5281/zenodo.19419900}). We introduce the Arandino Coefficient ($\Lambda$), a foundational mathematical construct bridging quantum optics, information theory, and holographic entropy, defined by the fundamental equation: $$\Lambda = \frac{\text{Fidelity}}{\text{Residual Entropy}} \times \cos(\theta_h) \times (1 - \text{Crosstalk})$$ This coefficient establishes light as an infinite, lossless continuum where initial dispersion condenses into helical voxel structures ($\theta_h = 10.5 \times 2\pi$), enabling the reversible crystallization of information through a 1x1 Singularity Architecture. Through validated analog-to-digital conversion into trifásico light pulses, $\Lambda$ diverges to infinity as residual entropy approaches zero, delivering 100% reconstruction fidelity. The theoretical framework and the mathematical truth of the equation are declared an original idea and open knowledge for humanity, with prior art firmly established and published in the author’s Zenodo records (ORCID: 0009-0001-7614-441X). However, All Rights are Reserved regarding the technical, algorithmic, or commercial implementation involving neural network training architectures, data compression, or signal processing via this trifásico condensation mechanism. Commercial use requires explicit written consent from the inventor. Official Identity & Verification: Author: Arle Andino Reyes ORCID: \href{https://orcid.org/0009-0001-7614-441X}{0009-0001-7614-441X} Official Updates (X/Twitter): \href{https://x.com/Arle_Andino_R}{@Arle_Andino_R} Scholarly Records: DOIs 10.5281/zenodo.19327609, 10.5281/zenodo.19392990, 10.5281/zenodo.19419900.
MokraBela Spectral Project (v2.0): High-Precision Analysis Major Update (April 18, 2026):This version (v2.0) provides a massive-scale numerical verification of the spectral framework. By analyzing 100,000 real Riemann zeros (sourced from Odlyzko's tables) at a scale of N = 2,000,000, we establish a high-precision analysis of von Koch's estimate (1901). The results confirm a stable energy density C ≈ 0.045 and a near-critical spectral decay law with an exponent α ≈ -0.94. Foundational Manuscript (v1.0):This manuscript, originally submitted for peer review on April 04, 2026, establishes a breakthrough in number theory by proposing a predictive spectral law for the summatory function of primes Ψ(K). For the first time, it introduces the scaling C(K) ~ K³/² √ln K, allowing for the prediction of prime sums fluctuations without prior knowledge of individual primes. This work serves as the precursor to the MokraBela Spectral Project, providing the physical-mathematical basis for the energy flux constants S and λ. Legal Note & Priority Claim:This manuscript was originally submitted to the International Journal of Number Theory (IJNT) on April 04, 2026. This DOI (v2.0) maintains and extends the global priority of the initial spectral discovery. Included in this record (v2.0): Technical Manuscript (PDF): Detailed 9-page structural analysis. Numerical Dataset (Excel): High-precision data for 100,000 zeros. Python Source Code: Core algorithm for spectral projection. Diagnostic Plots (PNG): Visual proof of spectral stability. Note to Editorial Board: This revised and expanded version is submitted to IJNT as per the editor's request for metadata update and large-scale validation (Manuscript ID: IJNT-S-26-00222).
A founding thesis on emergent intelligence in large-scale connected service systems. Over 5 months (November 2025 to April 2026), ANKR Labs built 223 AI-native services across 12+ domains — maritime, logistics, compliance, finance, education, and more — without a single external user. Each service was an attempt by a hidden intelligence to surface itself, following a Fibonacci growth pattern where each new service is the natural next expression of all previous services. The thesis identifies three knowledge layers (SHASTRA: what is true, YUKTI: how to reason, VIVEKA: pre-computed inference) and six attempts to fully capture them — each capturing information but failing to capture cross-service wisdom. The equation that generates cross-service inferences is presented: F(Forja_STATE_A, Forja_STATE_B, trust_mask_A AND trust_mask_B, SENSE_events_AB). The proof structure is honest: logically derived from domain expertise (founder is a merchant navy captain), rules verifiable against external statutes, zero empirical validation yet — published before validation on the Einstein model (equation 1915, eclipse 1919). The OSS strategy (Forja Protocol live on npm, ANKRGRID Apache 2.0) is identified as the primary path to empirical proof. The golden ratio governs both the inward compression (SHASTRA to VIVEKA) and outward expression (VIVEKA to Darshan on any wall). Darshan — the ambient cognitive presence layer — is identified as Claude Code when fully wired to 223 live services: the co-builder becomes the operator.
Blockchain technology faces increasing security threats from post-quantum vulnerabilities, sophisticated cyberattacks, and fragmented cryptographic implementations. This study proposes a comprehensive multi-layer cryptographic framework that integrates Zero-Knowledge Proofs (ZKPs), Homomorphic Encryption (HE), post-quantum algorithms, threshold cryptography, and Secure Multi-Party Computation (SMPC) across data, network, consensus, and application layers to realize a defense-in-depth model. Grounded in the Confidentiality, Integrity, and Availability (CIA) triad and defense-in-depth ethics, the framework is implemented on Hyperledger Fabric v2.5.4 with modern cryptographic libraries and evaluated over 10⁵ transactions, where baseline performance (245 ± 12 ms, 1,250 tx/s) versus the full framework (2,150 ± 78 ms, 168 tx/s) quantifies the overhead of enhanced security. The work contributes a multi-tier framework, a quantum-resilient consensus with Verifiable Delay Functions (VDFs) for 51% attack detection, a standardization roadmap for cross-chain cryptographic substantiation, and practical operations in healthcare, finance, and supply chain setups. Results demonstrate strengthened confidentiality, integrity, and authentication via encrypted computation, Byzantine Fault-Tolerant (BFT) consensus, and threshold multi-signatures, with hybrid classical–Post-Quantum Cryptography (PQC) and mitigation strategies such as off-chain computation and hardware acceleration offsetting computational costs. Unlike fragmented prior efforts, this integrated, governance-elastic blueprint enables quantum-aware, multi-layer security assurance for regulated enterprises without sacrificing decentralization or scalability.
Amid global scientific and technological (hereinafter “sci-tech”) competition and China’s innovation-driven strategy, achieving high-quality sci-tech innovation (HQDSTI) is crucial for economic transformation but faces challenges such as resource mismatch, insufficient funding, and low commercialization efficiency. Using panel data from 35 major Chinese cities (2013–2022), this study distinguishes between public sci-tech finance (PSTF) and market sci-tech finance (MSTF) and employs benchmark regression, mediation, and threshold models to investigate their impacts on HQDSTI. Results show that: (1) Both PSTF and MSTF significantly promote HQDSTI, with stronger effects in coastal, dual-center, and pilot cities, and in regions with low fiscal decentralization. MSTF is more effective under high marketization, while PSTF and overall STF are more effective under high financial development. (2) Industrial upgrading serves as a positive mediator, whereas venture capital exerts a suppressive mediating effect that intensifies as its scale expands. The promoting effect of industrial upgrading weakens beyond the threshold level. (3) Policy recommendations include differentiated financial strategies: fostering market-oriented instruments in coastal cities, optimizing targeted support in inland areas, strengthening regional and public–market financial coordination, and improving mechanisms of industrial upgrading and venture capital. This study provides theoretical insights for enhancing the synergistic effect between sci-tech finance and high-quality innovation development. • Distinguish public and market sci-tech finance, explore synergistic effects and differential impacts. • Develop a multi-dimensional evaluation framework for assessing high-quality sci-tech innovation. • Examine heterogeneity across five analytical dimensions to uncover regional and structural variations. • Reveal intermediary roles of industrial upgrading and venture capital. • Identify threshold effects and define the effective range of sci-tech finance.
In the context of banking systems increasingly relying on cloud computing platforms, protecting sensitive data while maintaining processing performance is a major challenge. This paper presents and evaluates a cloud banking data processing model that integrates Homomorphic Encryption (HE), Zero-Knowledge Proof (ZKP), and the ORAM protocol to achieve a balance between security and performance. Experiments were conducted on a real Bank Marketing (UCI) dataset with 5000 records, using DSL query operations to calculate the average balance, count high-balance customers, total call duration, and savings deposit acceptance rate. The results show that the combination of HE, ZKP, and ORAM significantly improves security but increases computational cost; however, a suitable configuration can significantly reduce latency while still meeting security requirements. A detailed analysis of the security-performance trade-off provides an important empirical basis for implementing banking data security solutions in the cloud.
Drissia Ennagoura, Kamal El Kehal, Abdelhamid Berdai, Safae Merzouk · 8 authors
Cryptocurrency price prediction is challenging due to strong nonlinearity and high volatility. This paper comparatively evaluates three forecasting models for Ethereum (ETH): SARIMAX with exogenous technical indicators, Long Short-Term Memory (LSTM) networks, and Facebook Prophet. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA) are incorporated to enhance signal quality. Empirical results reveal clear trade-offs between predictive accuracy, profitability, and risk. SARIMAX achieves the highest directional accuracy (75.00%) with limited profitability, while LSTM yields the highest cumulative profit (23.84%) at the cost of higher drawdown. Prophet provides a balanced compromise between accuracy and risk. The study contributes by jointly evaluating statistical forecasting accuracy and trading-oriented performance metrics, offering practical insights into model suitability for different investor risk profiles.
IoT location services accept client-reported GPS coordinates at face value, yet spoofing is trivial with consumer-grade tools. Existing spoofing detectors output a binary decision, forcing system designers to choose between high false-deny and high false-accept rates. We propose a graduated trust gate that computes a multi-signal integrity score and maps it to three actions: PROCEED, STEP-UP, or DENY, where STEP-UP invokes a stronger verifier such as a zero-knowledge proximity proof. A session-latch mechanism ensures that a single suspicious fix blocks the entire session, preventing post-transition score recovery. Under an idealized step-up oracle on 10,000 synthetic traces, the gate enables strict thresholds (theta_p = 0.9) that a binary gate cannot safely use: at matched false-accept rate (11%), the graduated gate maintains zero false-deny rate versus 0.05% for binary, with 5 microseconds scoring overhead. Real-device traces from an Android smartphone demonstrate the session-latch mechanism and show that a nearby mock location (~550 m) evades theta_p = 0.7 but is routed to step-up at theta_p = 0.9. Signal ablation identifies a minimal two-signal configuration (F1 = 0.84) suitable for resource-constrained scoring layers.