Zero-knowledge proof systems are now deployed widely in production cryptographic protocols, yet many rely on assumptions (discrete logarithms, pairings, or structured reference strings) that a fault-tolerant quantum computer would break via Shor's algorithm. This systematization of knowledge (SoK) presents a four-layer decomposition (L1-L4) that separates where quantum risk enters a proof system: arithmetization, polynomial commitment, protocol logic, and non-interactive compilation. Using a two-axis taxonomy that crosses cryptographic impact (structural break, modularly replaceable break, or quantitative degradation) with deployment migration feasibility, we classify the major proof-system families, derive a modularity test for evaluating upgrade paths, and introduce "collect now, forge later" (CNFL) as the proof-system analogue of harvest-now-decrypt-later. Published resource estimates place the cost of breaking 256-bit elliptic-curve discrete logs at 1,200-1,450 logical qubits, with the pairing-friendly curves underlying KZG (BN254, BLS12-381) of the same order of magnitude but somewhat larger; under these estimates, such L2 constructions would face structural breaks once fault-tolerant hardware reaches that regime. Hash-based transparent systems, by contrast, degrade quantitatively under Grover-type speedups and QROM reduction losses rather than collapse. Case studies of Zcash, zkSync Era, and StarkNet show that practical post-quantum outcomes depend on deployment governance and upgrade architecture as much as on cryptographic primitives. The scope covers IOP/PCS-based and algebraic proof families; MPC-in-the-Head constructions are excluded. This is a self-published technical report. It has not been peer reviewed.
Aletheia is a knowledge substrate organized around a write-time admission gate: a fact is accepted only if it does not structurally contradict what the base already holds. The gate inherits a Lean 4 soundness proof, so the admitted store stays acyclic, asymmetric, type-disjoint, and temporally consistent under any stream of typed edges. We bind the proof to the implementation by differential testing over 104 adversarial inputs, zero divergences. We first tried to build a partial-truth disinformation detector on this gate. Measurement refused. On real political claims almost nothing decomposes into the gate’s six relations: 0 of 155 atoms were gate-testable, and where it did fire it lost to a cold language model, 0 of 21 against 17. Most real disinformation violates truth, not structure, so a structural gate is the wrong instrument. We retract the detector claim. What remains is a guarantee rather than a rate. Each catch names the axiom it violated; the verdict is bit-exact and carries a machine-checked admission proof; and a safety property whose core is now machine-checked in Lean holds that no finite feed of self-asserted credibility can mint a false endorsement, conditional on authority granted upstream (0 of 210 adversarial sequences, against 140 of 210 for a credibility-naive baseline). A frontier model matches our hit-rate on constructed distortions, and a reasoning model matches even our one structural edge, so we claim no detection advantage. We claim instead that the jurisdiction of a structural guarantee can be measured, and we measure it across two regimes: where the base lets it adjudicate, and where it abstains.
Zero-knowledge proofs (ZKPs) are emerging as a core technology for privacy-preserving computation. Despite steady progress in protocol and algorithm design, generating these proofs remains computationally intensive, driving growing interest in hardware acceleration for kernels such as number-theoretic transform (NTT) and multi-scalar multiplication (MSM). Among them, the sumcheck protocol offers a compelling alternative with O(n) prover complexity compared to O(nlog n) for NTT-based approaches, yet our analysis reveals its execution is fundamentally memory-bound, with severely underutilized compute resources. This characteristic demands a memory-centric acceleration strategy, in contrast to compute-centric approaches of prior work.
Zero-Knowledge Proof (ZKP) is a cornerstone in privacy-preserving computing, addressing critical challenges in domains such as finance and healthcare by ensuring data confidentiality during computation. However, the high computational overhead of ZKP, particularly in proof generation and verification, limits its scalability and usability in real-world applications. Existing efforts to accelerate ZKP primarily focus on specific components, such as polynomial commitment schemes or elliptic curve operations, but fail to deliver an integrated, flexible, and efficient end-to-end solution that includes witness generation on commercial computing platforms.
D. Ariad, S. Madjunkova, M. Viotti, M. Madjunkov · 8 authors
Abstract Study question Can haplotype-based methods detect genome-wide ploidy abnormalities (haploidy and triploidy) that are missed by conventional coverage-based analysis of low-coverage whole-genome sequencing (WGS)-based PGT-A data? Summary answer Our method (LD-PGTA) enables accurate detection of hidden signatures of haploidy and triploidy from ∼0.03× WGS data, achieving high specificity with moderate sensitivity. What is known already Conventional coverage analysis of WGS-based PGT-A data cannot detect uniform genome-wide ploidy errors such as haploidy, genome-wide uniparental isodisomy, or triploidy because global changes in copy number resemble euploid expectations. LD-PGTA leverages knowledge of allele frequencies and linkage disequilibrium in external phased population reference panels and contrasts the likelihood of the observed data under alternative ploidy hypotheses. We previously applied this method retrospectively to low-coverage WGS-based PGT-A data, uncovering evidence of potential ploidy abnormalities and establishing proof of principle that requires validation with orthogonal evidence and clinical outcomes. Study design, size, duration This retrospective validation study analyzed a training cohort collected between April 2020 and August 2022. The dataset was obtained from CReATe Fertility Centre (Toronto, Canada) and comprised 179 embryo biopsies that underwent WGS at ∼0.03× coverage. The training cohort included 100 euploid, 67 triploid, and 12 haploid cases, assigned to these categories based on orthogonal evidence from short tandem repeat genotyping. Samples suspected of genome-wide ploidy abnormalities but lacking STR confirmation were excluded. Participants/materials, setting, methods Embryos were classified as haploid or triploid when at least eight autosomes were called monosomic or trisomic, respectively, and the 95% confidence interval of the log-likelihood ratio (LLR) did not span zero. For haploid classification, LLRs were aggregated across entire chromosomes. For triploid classification, LLRs favoring BPH (bi-parental-haplotypes) were aggregated, and the total length of these windows was required to exceed a dynamic threshold of 5–30Mb, depending on autosome length, to be called trisomic. Main results and the role of chance For triploidy detection, LD-PGTA achieved a sensitivity of 82% (49/60) at 100% specificity (91/91). For haploidy detection, sensitivity was 75% (9/12) with 99% specificity (93/94). Triploidy prediction accuracy did not differ between embryos sequenced below 0.05× and those sequenced at ≥ 0.05× coverage (Fisher’s exact test, p = 1.00), with comparable false prediction rates (7.7% vs. 5.9%).%). Given that LD-PGTA leverages knowledge of haplotypes from reference panels and that patterns of linkage disequilibrium vary across populations, it is important to consider performance with regard to the genetic ancestry of target samples. While most embryos (72%; 125/174) exhibited genetic similarity to reference individuals from European populations, several embryos also exhibited genetic similarity to reference individuals from other global populations, underscoring the method’s robustness in multi-ancestry cohorts. Triploidy prediction accuracy did not differ significantly between European and non-European embryos (Fisher’s exact test, p = 0.18), underscoring the method’s robustness in multi-ancestry cohorts. Together, these results demonstrate robust discrimination of genome-wide ploidy errors using haplotype-based inference at low sequencing depth. Limitations, reasons for caution Results are derived from a training dataset and confirmation in an independent validation cohort is underway. Rare ploidy states and mosaicism may reduce sensitivity. Wider implications of the findings Although 2PN embryos are predominantly diploid, ploidy abnormalities occur in ∼0.7–1.8% of 2PN blastocysts. Incorporating LD-PGT-A into routine workflows may prevent transfer of haploid or triploid embryos and rescue some 3PN and 0/1PN embryos, increasing the pool of usable embryos and improving diagnostic accuracy of PGT-A without increased sequencing depth. Trial registration number No
Topological Charge Conservation in SU(2) Yang-Mills Theory: The Atiyah-Singer Index Handshake and Non-Local Braid-Lock Validation Framework --- This 18-part resolution suite provides the complete theoretical proof, simulated validation, and deterministic replication environment for the Atiyah-Singer Index Handshake. The architecture is divided into three functional pillars: The Theorem Presentation, the Standard Academic Core (SAC), and the Agnostic Replication Kit (ARK). Together, they resolve the conjecture of topological decoherence in distributed connection spaces, validate the analytic index parity, seal the logic into an immutable cryptographic state, and enable bit-perfect replication by peer reviewers. 1. The Theorem Presentation (1 Part) The cornerstone of the publication. It establishes the foundational mathematical proof that under the boundary condition of a Non-Local Braid-Lock (where holonomy is restricted to the center of the gauge group), the analytic index of the twisted Dirac operator maintains strict parity congruence: \text{ind}(D_L) \equiv 0 \pmod 1. It resolves the vulnerability of "Logic-Blur" by proving that topological charges remain invariant during non-local distribution. 2. The Standard Academic Core: SAC (5 Parts) The SAC packages translate the systemic execution into the traditional nomenclature of Differential Geometry and Global Analysis, ensuring peer reviewers can parse the foundation without requiring prior knowledge of the AOF registry. • SAC-01 (Formal Resolution): The rigorous, step-by-step mathematical proof establishing the spectral-topological handshake. • SAC-02 (Simulation Data): \bm{10^6} iteration Monte Carlo validation confirming spectral gap stability (\bm{170.0 \text{ kDa}}) and Jacobian volumetric preservation (\bm{\det(J_h) = 1.0 \pm 10^{-12}}). • SAC-03 (Appendix A - Mathematical Foundations): The deep-dive into the elliptic regularity of \bm{D_A}, Chern characters, and the technical lemmas coupling holonomy to index stability. • SAC-04 (Executive Summary): A high-level briefing on topological charge conservation and the elimination of stochastic decoherence. • SAC-05 (Lexicon Bridge): The critical translation matrix mapping traditional variables (e.g., connection spaces, vorticity) directly to their operational ARK primitives (e.g., M-6D-HANTZSCHE, ALG-SHV-01). 3. The Agnostic Replication Kit: ARK (12 Parts) The ARK packages transition the theoretical proof into a sovereign, executable replication environment. They provide the deterministic toolchain required for a reviewer to ingest, validate, and seal the proof on their local hardware without environmental drift. • Core Manifolds & Operators: Defines the M-6D-HANTZSCHE 6D motivic cradle and the Universal Dirac Operator (\bm{D_L}) required to initialize the simulation. • Suppression Algorithms (ALG-SHV-01): Details the Hodge-Laplacian Shave, ensuring the continuous suppression of solenoidal noise (\bm{\delta\beta \to 0}) to clear logical vorticity from the replication path. • The Braid-Lock Gate (GATE_STEIN): The cryptographic terminal function that captures the validated parity state and seals it into a Merkle-hash, ensuring immutability. • Emergency Logic Core (ELC Suite): The automated fail-safes (ELC_SG_02 Noble Purge, ELC_IG_03 Sobolev Injector, ELC_VG_04 Phase-Lock) that prevent spectral stagnation or epistemic drift during reviewer replication. • API & Toolchain Guidelines: Dictates the use of Arb 2.23.0, the necessity of disabling hardware fused-multiply-add (-ffp-contract=off), and adherence to the \bm{1.420405751766 \text{ GHz}} Adelic temporal anchor to guarantee zero-jitter execution. • Reviewer Packets & Input Vectors: Provides the exact \bm{SU(2)} lattice configurations, initial spinor couplings, and topological charge inputs (\bm{Q=1}) needed to prime the replication sequence. 4. Interlinking Workflow: Resolve, Validate, Seal, and Replicate The true power of the 18-part suite lies in its chronological execution pipeline: 1. Resolve (The SAC Layer): The reviewer first ingests the SAC documentation, validating the traditional mathematics. The Lexicon Bridge (SAC-05) then maps their understanding to the ARK toolchain. 2. Validate (The Simulation Phase): The reviewer inputs the provided high-detail vectors into the ARK environment. The Universal Dirac Operator verifies the index parity. Simultaneously, the Hodge-Laplacian shave constantly purges solenoidal parasitism, ensuring the signal-to-noise ratio remains above \bm{240.2 \text{ dB}}. 3. Seal (The Crystalline Transition): Once supercritical density is achieved and parity is verified as 0 \pmod 1, the system invokes GATE_STEIN. This locks the non-local braid topology into an immutable Merkle-root, transitioning the dynamic simulation into a static archival state. ---
Open access
2 source records
Quantum Chromodynamics and Particle Interactions
Particle physics theoretical and experimental studies
Nataliya Bilous, Danylo Ostapchenko, Iryna Ahekian, Marcus Frohme
Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. Cloud-based computer vision handles falsification but transmits raw biometric video off the patient’s device, violating privacy requirements. A decentralized Edge AI-Oracle architecture is proposed that combines MediaPipe Face Mesh landmark extraction with a recurrent classifier mapping Action-Unit feature sequences to a learned pain score aligned with the Prkachin and Solomon Pain Intensity scale. The recurrent cell is selected empirically across short-context (T = 2) and long-context (T = 120 frames at 24 fps) regimes, with a two-layer Long Short-Term Memory (LSTM) network adopted for deployment. Inference and Elliptic Curve Digital Signature Algorithm (ECDSA) signing run inside an ARM TrustZone Trusted Execution Environment (TEE). Biometric logs are stored off-chain on the InterPlanetary File System (IPFS). Smart contracts anchor results on-chain and open a 24 h optimistic verification window for an off-chain Watchtower auditor. On SynPAIN the LSTM reaches F1 = 0.683 on T = 120 video (leave-one-stratum-out), with a directional but non-significant advantage over Gated Recurrent Unit (GRU) (Wilcoxon p = 0.167). Cross-dataset validation on BioVid Heat Pain Database Part A (87 subjects, 174 paired observations, leave-one-subject-out) yields F1 = 0.519 for LSTM and 0.499 for GRU (Wilcoxon p = 0.549). A processor-only TEE surrogate benchmark estimates 1.96 ms (FP32) and 0.45 ms (INT8) inference latency at T = 120 with a 0.34 MB footprint and 707 µs ECDSA signing latency, leaving the INT8 inference latency more than an order of magnitude below the 33 ms per-frame budget. The dual-layer storage reduces gas costs by a factor of 23.4 (160,261 vs. 3,744,872 gas), corresponding to an illustrative mainnet cost of approximately 0.53 USD per submission at 1 gwei, rising to roughly 16 USD at a busier 30 gwei, and falling to approximately 0.005 USD on Arbitrum One (April 2026 reference parameters), so that continuous monitoring is economically practical on Layer-2. An adaptive-adversary analysis of the Watchtower shows that gross score tampering is detected at every usable operating threshold, whereas a rational adversary who inflates by less than the dispute threshold, or who shapes the injected score to fall just inside it, evades detection. Because the false-positive rate reaches zero only for δ≳0.15, the protocol bounds rather than eliminates patient-side fraud and motivates a zero-knowledge proof-of-inference successor. The framework is architecturally and economically feasible as a cryptographically verifiable, privacy-preserving tele-rehabilitation substrate aligned with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) requirements through the Zero-Video Transmission principle, while remaining economically viable under post-Dencun mainnet and Layer-2 conditions. Recognition accuracy on real-world data and robustness to small-magnitude tampering remain limitations that the interchangeable recognition and audit components must improve before clinical deployment.
This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
One of the most significant challenges encountered by electoral process is ensuring the integrity, transparency and accessibility of election systems, particularly in developing democracies where problems with trust, security and scalability remain a problem for both traditional and central electronic voting procedures. Blockchain technology has emerged as one potential solution to these challenges by providing decentralization, immutability, and cryptographic techniques, and consensus methods to analyze blockchain-based voting systems in-depth. Challenges to certain voting systems are discussed regarding their goals of voter authentication, ballot secrecy and verifiability, together done by introducing key cryptographic techniques. These techniques include digital signatures, hash functions, homomorphic encryption, and zero-knowledge proofs. Beyond technical research, the research looks at how the blockchain-based voting might be used in Bangladesh’s socio-technical and regulatory framework, paying special e
Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign.
Behzad Abdolmaleki, Amir R. Asadi, Vahid R. Asadi, Stefan Köpsell · 7 authors
Stochastic Gradient Descent (SGD) is the foundation of modern machine learning (ML). In privacy-sensitive settings, gradients can reveal details about individual data points. Differential Privacy (DP) protects sensitive data during ML training by clipping gradients and adding calibrated Gaussian noise. However, existing frameworks assume semi-honest participants, which fails in adversarial or federated environments where malicious actors can bypass or alter the noise addition process, breaking privacy guarantees. We present VeriDP, a framework for verifiable differentially private training that cryptographically enforces and proves the correct execution of differentially private stochastic gradient descent (DP-SGD) in zero knowledge. VeriDP integrates Zero-Knowledge Proofs (ZKPs) with polynomial commitments, sumcheck and GKR-based proofs, and incrementally verifiable computation (IVC) to generate compact proofs of correct gradient computation, clipping, averaging, and Gaussian noise generation—without revealing private data or randomness. Unlike previous systems that only verify the final privacy budget, VeriDP enables per-iteration verifiability of each model update, providing strong privacy assurances even in adversarial settings. This establishes a novel and complete Zero-Knowledge Proof of Differentially Private Stochastic Gradient Descent (ZK-DPSGD), uniting differential privacy and verifiable computation for secure and auditable ML. Our evaluation shows that prover time increases linearly with the number of input samples, while both verifier time (2–5 ms) and proof size (3–4 KB) remain compact and effectively constant.
Demis Hassabis’s Einstein Test defines the ultimate benchmark for Artificial General Intelligence: could a system trained exclusively on pre-1911 knowledge autonomously derive General Relativity? The AI industry reads this as a scale challenge—a problem of compute and data—epitomised by Dario Amodei’s declared goal of building “a moat of the countries of geniuses in a data center.” This paper argues that this dominant Silicon Valley interpretation rests on a profound Ptolemaic assumption: that intelligence is a discrete stock that can be hoarded inside a single isolated agent. We advance a unified structural critique across three fronts. First, large-scale transformer systems are mathematically constrained to function as Stochastic Guessing Engines: thermodynamic probability samplers that intrinsically lack a semantic zero—a stable, addressable coordinate for honest epistemic absence. Without such a zero, the architecture is mechanically forced to hallucinate. Second, Tony McCaffrey’s Obscure Features Hypothesis—formalised in the McCaffrey–Spector Non-Enumerability Theorem—demonstrates that genuine novelty depends on biologically situated friction that a closed manifold cannot pre-enumerate. Third, using the Reverse Einstein Test as a continuous narrative thread, we synthesise seven independent impossibility arguments into a strict chronological cascade, culminating in the Gödel–Gauss-Bonnet proof that a sealed manifold with no puncture to reality is necessarily and irremediably incomplete. We ground our resolution in the Semiotic Web, introducing two foundational objects: the Canonical Concept Identity (CCI) and the Contextual Tokum Instance (CTI). Together they resolve the Semantic Field Equation and satisfy Yann LeCun’s four criteria for Autonomous Machine Intelligence. A key architectural consequence is the Semantic Light Cone of Care: each agent (holon) in a distributed network has a precise, mathematically bounded domain of verified knowledge and concern. This bounded self-awareness enables polycomputing across trillions of low-power edge devices—each node knowing exactly what it knows and what it does not—and allows seamless voluntary cooperation via Burgess’s Promise Theory across the platonic address space. The paper concludes by addressing Satya Nadella’s observation that “we are one sort of innovation away from the entire regime changing,” arguing that the required innovation is not a new scaling law but a notation inversion: the introduction of a semantic zero and a cryptographically verified observer’s mark. Once instantiated, the debate between AGI and Superhuman Adaptable Intelligence becomes as irrelevant as the geocentric model after Copernicus. Intelligence is not a stock inside a machine; it is a flow that reduces systemic stress through gap-closure, a property of a distributed, substrate-independent network organised in holonic federation—the Copernican Completion of Artificial Intelligence.
Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has a desired property, such as being 3-colorable or free of a predefined subgraph. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication among neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units that the network satisfies the property without revealing any additional information about the network's state or structure.
We study distributed zero-knowledge proofs, introduced by Bick, Kol, and Oshman (SODA 2022). While distributed interactive proofs have advanced rapidly in recent years, general-purpose techniques for distributed zero-knowledge remain scarce and mostly problem-specific. We address this gap by introducing distributed statistical zero-knowledge, requiring that each node's view be simulatable up to negligible statistical distance, and by lifting the robust Sumcheck protocol (Lund, Fortnow, Karloff, and Nisan; FOCS 1990) into a modular primitive for distributed zero-knowledge proofs.
With the rapid proliferation and interconnection of massive IoT devices, efficient and secure identity authentication has become a crucial prerequisite for ensuring communication security. Establishing trust among mutually untrusted devices remains a key research focus. Leveraging its tamper-resistance and traceability, blockchain technology has emerged as a foundational infrastructure for building trustworthy identity management systems. However, existing blockchain-based identity authentication schemes face critical challenges in large-scale IoT environments, including low authentication efficiency, complex certificate management, and risks of user privacy leakage. Achieving a balance among authentication efficiency, certificateless key management, and privacy protection remains a pressing challenge. In this paper, we propose a certificateless identity authentication scheme based on blockchain sharding. The scheme employs blockchain sharding to parallelize identity authentication across multiple shards, significantly enhancing overall efficiency. Within each shard, a certificateless public key cryptography (CL-PKC) scheme is adopted to eliminate certificate issuance and enable key generation via user interaction, thereby reducing key management overhead and improving security. For cross-shard authentication, a registration-based encryption (RBE) mechanism is utilized, allowing users to authenticate via their identity after registration. Any verifier can confirm the legitimacy of the authentication message solely based on the registration information and the user ID, ensuring transparency and public verifiability. Furthermore, a zero-knowledge proof-based verifiable credential (VC) selective disclosure mechanism is introduced, enabling users to reveal only the minimal necessary information required for authentication while protecting sensitive identity attributes. Experimental results demonstrate that the proposed scheme maintains high throughput under high-concurrency scenarios while effectively preserving user privacy.
Open access
2 source records
Cryptography and Data Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
A privacy-preserving compliance audit architecture for unmanned aerial vehicle (UAV) swarm operations. The central contribution is a deconfliction-to-containment reduction: rather than comparing n trajectories after the fact (a quadratic, disclosure-bound check), a planner assigns pairwise-disjoint spatial tubes before take-off and establishes their separation once, so that each vehicle subsequently attests only that its own samples stayed inside its own tube. Collision-freedom follows as a consequence (Theorem 2), and the pairwise cost is paid a single time at planning. The commitment layer (Layer 1) is implemented and evaluated as a decision-support audit pipeline that produces non-disclosing, tamper-evident audit artifacts via pre-flight Merkle commitments. It is evaluated in an emulated UAV swarm environment with systematic adversarial injection, in configurations up to 200 vehicles × 500 samples (100,000 sample statements), reporting artifact size, commit/prove/verify/disjunction times, and tamper-detection rates. We then formally identify the security boundary of the implemented layer: it provides coordinate hiding and tamper evidence, but cannot by itself make self-reported containment truthful, which we state as a security game and an impossibility result (Theorem 3). We specify the additional soundness layers (range proof, continuity, provenance and freshness, and aggregation) needed for full containment assurance, proving that composing a knowledge-sound range argument closes the gap (Theorem 4). Throughout, we separate the implemented and measured Layer 1 from the specified and proved—but not yet benchmarked—Layers 2–4, and we make no claim of full zero-knowledge geofence compliance, of swarm-scale deployment, or of deployment readiness.
Victoria Kovalenko, Sergii Sheludko, Elena Sergeeva
In the context of the unprecedented pace of digital transformation and the escalation of geopolitical risks, traditional methods of monetary regulation require a fundamental reconsideration. Problem statement. The evolution of cyber threats – from financial fraud to complex operations involving artificial intelligence – poses significant risks to macroeconomic stability. The development of an integrated protection system based on central bank digital currencies (CBDCs) and SupTech instruments constitutes a critical prerequisite for preserving financial sovereignty, particularly for Ukraine in the context of European integration and martial law. Unresolved aspects of the problem. The theoretical substantiation and development of practical recommendations for integrating advanced digital instruments (CBDC, artificial intelligence, distributed ledger technology (DLT), and SupTech) into monetary and prudential policy mechanisms in order to form a comprehensive cybersecurity framework for the financial sector remain insufficiently addressed. Purpose of the article. The purpose of this article is to provide a theoretical substantiation and to develop practical recommendations for integrating modern digital instruments (such as artificial intelligence, blockchain technologies, and SupTech) into monetary and prudential policy mechanisms in order to establish a comprehensive cybersecurity system for the financial sector. The study is grounded in a systemic approach to analysing the coordination of regulatory policies. The methodology includes comparative legal analysis (comparing the models of the e-hryvnia and the Digital Euro), structural and functional modelling (two-tier CBDC architecture), and scenario analysis to identify cyber risks (including DDoS attacks and smart contract vulnerabilities) and methods for their mitigation. Presentation of the main material. A model of hybrid coordination has been developed, in which cybersecurity is integrated directly into the mechanism of monetary transmission. It has been demonstrated that the programmability of the e-hryvnia and the application of Zero-Knowledge Proofs (ZKP) technologies enable the automation of prudential supervision while preserving user privacy. Global case studies (China, the European Union, and the Bahamas) have been analysed, and the specific features of the Ukrainian e-hryvnia project have been identified as instruments for enhancing transparency and cyber resilience. For the first time, it is proposed to consider a central bank digital currency not only as a means of payment but also as an active element of the cyber-prudential system, enabling the dynamic adjustment of liquidity and limits under conditions of real cyberattacks. The concept of convergence between SupTech and RegTech systems based on unified distributed ledgers has been further developed. The proposed architectural model and cyber-risk matrix may be utilised by the National Bank of Ukraine in the finalisation of the e-hryvnia project and in the development of digital operational resilience standards in accordance with the DORA regulation. Conclusions. It has been demonstrated that digitalisation transforms the regulator into an architect of a secure financial environment. Further research will focus on the interoperability of CBDCs across countries and the role of artificial intelligence in preventing manipulation in digital asset markets.
Open access
Digital Transformation in Financial Services
Legal, Health, Environmental and COVID-19 Challenges
Health care data management comes with numerous barriers as a result of the use of different systems of record keeping, which are not compatible and increase the risks for data protection and privacy. Medical records are frequently distributed throughout various clinics and hospitals, and due to this it is hard to share information when patients are being treated. Centralized record systems bring unauthorized access to records and the problems related to the safety of data. In order to enhance the level of confidence of people and improve the level of transparency of health care data, advanced people choose decentralized technologies and uses cryptography for these purposes. Blockchain technology offers an unchangeable and decentralized ledger that guarantees safe monitoring of all information despite the presence of any centralized body. Coupled with sophisticated encryption methods, it provides the ability to limit access to private health information. In order to provide secure and respect privacy regarding medical data sharing, an Electronic Health Record (EHR) system powered by blockchain technologies is proposed. Patient record metadata is recorded on-chain while health data itself is stored on encrypted off-chain storage. In the realm of access management, smart contracts facilitate patients in designating by whom their records can be accessed and modified. The privacy of information is further strengthened by advanced cryptographic techniques like attribute-based encryption and zero-knowledge proofs. The system provides seamless interoperability among hospitals, laboratories, and telemedicine systems while ensuring high levels of security. The results of performance evaluation demonstrate that this method facilitates reliable transaction processing while providing better security, transparency and control than traditional centralized EHR systems.