The barriers toward the economy for underbanked communities are made much deeper due to their access to formal financial services. In recent years, mobile infrastructure and blockchain technology have opened new corners for the establishment of digital identity systems that show promise enabling secure and verifiable identity for greater access to finance. This study contrasts the systems through very rigorous casework in India and Uganda, and a technical comparison of mobile-based concurrent mobile and blockchain-enabled self-sovereign identity (SSI) frameworks. Observations from survey data supplement them by indicating trends on user adoption and challenges. Results indicate that digital identities give an additional entry into financial services anywhere between 20 to 30 percent for the under banked. Although they are the most affected by resistance posed by tried and tested issues such as low digital literacy, lack of infrastructural facilities, trust deficit among consumers, and so on. A hybrid digital identity framework with Zero-Knowledge Proofs (ZKPs) is thus proposed, which combines the widely accepted benefits of mobile systems with the enhanced privacy and data protection features offered by the blockchain technology. The model is constructed based on affordability, interoperability, and user agency; thus, it is scalable and inclusive to the economic realities faced by sub-Saharan Africa and South Asia.
Self-Sovereign Identity (SSI) frameworks enable individuals to receive and present digital credentials in a user-controlled way. Revocation mechanisms ensure that invalid or withdrawn credentials cannot be misused. These revocation mechanisms must be scalable (e.g., at national scale) and preserve core SSI principles such as privacy, user control, and interoperability. Achieving both is hard, and finding a suitable trade-off remains a key challenge in SSI research.This paper introduces UPPR, a revocation mechanism for One-Show Verifiable Credentials (oVCs) and unlinkable Anonymous Credentials (ACs). Revocations are managed using percredential Verifiable Random Function (VRF) tokens, which are published in a Bloom filter cascade on a blockchain. Holders prove non-revocation via a VRF proof for oVCs or a single Zero-Knowledge Proof for ACs. The construction prevents revocation status tracking, allows holders to stay offline, and hides issuer revocation behavior. We analyze the privacy properties of UPPR and provide a prototype implementation on Ethereum. Our implementation enables off-chain verification at no cost. On-chain checks cost 0.56–0.84 USD, while issuers pay only 0.00002–0.00005 USD per credential to refresh the revocation state.
The MegaETH blockchain introduces new twists into improving healthcare transactions in efficiency and safety. MegaETH follows the hybrid consensus approach of PoS with BFT for solving some of the big issues in healthcare data management. Its strong encryption and zero-knowledge proof further enable significantly better protection of sensitive patient data, while reducing the risk of data breaches. It manages healthcare transactions fast and reliably, with a remarkable transaction throughput of about 10,000 transactions per second and a block duration of about one minute. Another important virtue of MegaETH architecture is that it uses less energy compared to more conventional Proof of Work systems. The demands of healthcare data are effectively managed with the scalability of the platform, underpinned by layer-2 solutions and sharding. MegaETH also illustrates excellent interoperability, as it will integrate with the existing systems of an institution and strictly abide by the rule of law. Moreover, smart contract executions are rather cheap, which enhances fraud prevention and accelerates administrative processes. The impacts from the adoption of MegaETH will be huge on reducing costs, ensuring data integrity, and finally improving patient care. Among the different options for solving current and future issues in health transaction administration, MegaETH is one of a kind.
Abstract Purpose The purpose of this study is to adapt a Bayesian dual‐virtual non‐contrast (VNC) method by integrating prior anatomical knowledge from AI‐based multi‐organ segmentation and to generalize it for spectral photon‐counting CT (PCCT) with an arbitrary number of energy channels. Methods A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open‐access whole‐body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast‐enhanced dual‐energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy‐relevant parameters such as electron density () and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non‐contrast scans by setting contrast agent uptake to zero. Results AI‐based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and . Mean absolute water‐equivalent path length (WEPL) errors confirmed the superiority of segmentation‐assisted PCCT over other methods. Conclusion This proof of concept demonstrates a flexible, AI‐assisted Bayesian framework for extracting quantitative information from contrast‐enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is needed. Background Quantitative VNC methods offer the potential to extract radiotherapy‐related parameters from contrast‐enhanced spectral CT without the need for additional non‐contrast imaging. However, the inherently ill‐posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced techniques.
Blockchain has emerged as a promising technology for enabling decentralized, tamper-evident, and auditable data sharing among multiple untrusted parties. However, practical deployments in distributed computing environments face a persistent trade-off between scalability and privacy. Public blockchain networks often expose transactional metadata, compromising confidentiality, while privacy-preserving blockchains—such as those leveraging zero- knowledge proofs (ZKPs)—typically suffer from reduced throughput and increased latency due to the computational overhead of proof generation and verification. Similarly, scalability-enhancing techniques like Layer- 2 rollups, sharding, and state channels often provide minimal privacy guarantees, leaving sensitive metadata vulnerable to inference attacks. This paper proposes a privacy-preserving and scalable blockchain architecture designed specifically for secure data sharing in distributed systems, such as federated cloud platforms, healthcare data networks, IoT ecosystems, and inter-bank settlements. The architecture integrates Layer-2 zero- knowledge rollups with a modular Layer-1 settlement layer (Ethereum or Hyperledger Fabric), decentralized storage (IPFS/Filecoin), and fine-grained access control mechanisms. By batching transactions off-chain, generating succinct ZK proofs for validity, and committing only aggregate proofs and state roots to the base chain, the system achieves both confidentiality and high throughput. The architecture is deployed in a Kubernetes-orchestrated environment, enabling horizontal scaling, automated failover, and comprehensive observability through Prometheus, Grafana, and Jaeger. A prototype implementation demonstrates a throughput improvement of up to$5.8 \times$over baseline privacypreserving blockchains, with latency remaining within acceptable limits for distributed applications. Our evaluation framework compares the proposed design against three baselines— Layer-1 only, Layer-1 + privacy, and Layer- 1 + scalability—and includes metrics such as throughput, latency, cost, privacy efficacy, and fault tolerance. The results indicate that combining privacy-preserving Cryptography with scalable rollup architectures is both feasible and beneficial for real-world distributed systems, offering a compelling pathway toward secure, high-performance blockchain applications.
Anders Malta Jakobsen, Oliver Holmgaard, Daniele Dell’Aglio, Michele Albano
As Ethereum is one of the most popular blockchains, it is naturally targeted by various attacks, aiming, for example, to disrupt the service or steal tokens. Among these, in deanonymization attacks, an adversary can obtain validator IP addresses and then perform a Denial-of-Service attack on them. To mitigate this attack, the Ethereum foundation is proposing Whisk, a Single Secret Leader Election protocol that uses a zero-knowledge proof called Curdleproofs to prove the validity of a shuffle of validators. One limitation of Curdleproofs is the shuffle size, which must be a power of two, restricting the number of validators that can be included. This paper overcomes this limitation by proposing CAAUrdleproofs, a modified version of Curdleproofs that incorporates Springproofs. Our experiments show that CAAUrdleproofs offers a performance advantage for any shuffle size that is not a power of two and that this advantage increases as the shuffle size decreases below a power of two.
Zeinab Alipanahloo, Michael Duchesne, Kaiwen Zhang
RzkFL is an end-to-end, privacy-preserving machine-learning framework that fuses Federated Learning (FL) with recursive zero-knowledge proofs (ZKPs) to protect data, models, and users while unlocking verifiable inference. Models are trained entirely on local devices, so sensitive data never leave the premises. The resulting model can be monetized by offering verifiable predictions on a pay-per-use basis. During inference, each customer independently computes predictions using private data, making it essential to verify that these inference results are computed correctly and honestly. Unlike existing approaches that rely on heavy communication or centralized trust assumptions, RzkFL allows each customer to generate a cryptographic proof of correct local inference, which can be succinctly verified without revealing input data or model parameters either by the customer or a third party. The core innovation lies in the use of recursive ZKPs, enabling each customer to generate small, composable proofs for intermediate layers of neural network inference. These proofs are then recursively aggregated into a single succinct proof using the Nova proof folding scheme. Nova’s design eliminates the traditional sequential dependency of recursive proofs by enabling incrementally verifiable computation through a folding scheme. RzkFL supports on-chain verification via Ethereum smart contracts, allowing AI results to flow directly into financial workflows. A decentralized file storage system maintains the integrity and availability of the global model. We introduce specialized circuits for input, hidden, and output layers to optimize proof generation time and gas costs. The customer can generate proof for the entire inference computation or delegate the proof generation for the intermediate layers and the output layer to another party. The design suits privacy-preserving machine learning scenarios where customer devices are resource-constrained. Our results show that RzkFL can significantly reduce proof size and verification costs while maintaining privacy, integrity, and scalability in federated inference. This makes it a compelling approach for real-world decentralized AI systems requiring strong verifiability guarantees.
Ghislain Nkamdjin Njike, Anh-Tu Hoang, Stefan Schulte
Distributed learning (DL) is gaining popularity as it enables clients (e.g., AI Agents) to enhance their machine learning (ML) models’ performance by exchanging knowledge without revealing private datasets. State-of-the-art DL approaches primarily focus on transferring knowledge between heterogeneous clients with diverse model architectures, connecting clients with those that can improve their models, and protecting data privacy. However, they overlook the threat of malicious clients that potentially downgrade the models’ performance by sharing inaccurate knowledge or excluding high-performing clients from the training procedure.Therefore, we introduce the Zero-Knowledge Blockchain-Based Knowledge Distillation Learning Framework (zkBKD). In zkBKD, heterogeneous clients communicate with a blockchain network to discover high-performing clients, verify zero-knowledge proofs (ZKPs) to ensure the correctness of the knowledge shared from other clients, and vote to eliminate malicious clients. We analyze security and privacy risks and show that zkBKD prevents membership, poisoning, and collusion attacks. We conduct extensive experiments on two standard datasets across heterogeneous clients with four model architectures. The experimental results demonstrate that zkBKD relatively improves the average model accuracy of all clients by 25.71%. Even lightweight models such as ResNet-2 achieve up to a 103.35% accuracy gain compared to independent training.
Dingsen Shi, Chris Tsu, Ying He, Alex Goss · 8 authors
Zero-Knowledge Proofs (ZKPs) are becoming a foundational technology for scalable and privacy-preserving blockchain systems, especially through applications like zkRollups. However, the computational intensity of proof generation continues to limit real-world deployment. We present ZKPU, a hardware-software co-designed ZK accelerator that combines native NVMe integration—ensuring seamless compatibility across existing server and edge infrastructure—with a modular RISC-V System-on-Chip (SoC) architecture that opens the path to eliminating host–device communication bottlenecks. ZKPU is designed to flexibly support a wide range of ZK workloads; in this work, we demonstrate its capabilities by implementing and optimizing multi-scalar multiplication (MSM), a core bottleneck in many zk-SNARK systems. Built using the Chipyard framework and equipped with dedicated modular arithmetic units, ZKPU achieves significant performance and energy efficiency improvements over CPU, GPU, and FPGA baselines. Our results highlight ZKPU as a practical and forward-compatible foundation for scalable ZK acceleration in modern decentralized systems.
Hasan Akgul, Mari Eplik, Javier Rojas, Aina Binti Abdullah · 5 authors
ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a policy-grounded decision layer and end-to-end zero-knowledge proofs of inference. The encoder uses masked spectral pretraining with phase-consistency regularization, plus a light cross-modal alignment that ties RF features to compact, human-interpretable policy tokens. To reduce unsafe actions under distribution shift, we add a calibrated selective-abstention head; the chosen risk-coverage operating point is registered and bound into the proof. We implement a four-stage proving pipeline: (C1) feature sanity and commitment, (C2) threshold and version binding, (C3) time-window binding, and (C4) PLONK-style proofs that the quantized network, given the committed window, produced the logged action and confidence. Micro-batched proving amortizes cost across adjacent windows, and a gateway option offloads proofs from low-power devices. The system integrates with differentially private federated learning and on-device personalization without weakening verifiability: model hashes and the registered threshold are part of each public statement. Across activity, presence or intrusion, respiratory proxy, and RF fingerprinting tasks, ZK-SenseLM improves macro-F1 and calibration, yields favorable coverage-risk curves under perturbations, and rejects tamper and replay with compact proofs and fast verification.
Healthcare IoT systems must balance the need for continuous monitoring with strong guarantees of privacy and trust. We present ProofHealth, a zero-knowledge proof–based framework that shifts verification to the edge by generating zk-SNARKs on smartphones. In this design, wearable data is encrypted and accompanied by proofs that ensure only valid submissions are admitted to cloud storage, even on untrusted networks. We implement and evaluate ProofHealth under varying batch sizes, measuring latency, throughput, and proof size. Results show batching significantly improves per-sample efficiency while proof sizes remain constant at sub-kilobyte scale, enabling lightweight communication suitable for constrained devices. This demonstrates the practicality of proof-at-the-edge healthcare monitoring and establishes ProofHealth as a novel approach to secure and privacy-preserving health data collection.
Blockchain technology has revolutionized digital transactions by offering decentralization, transparency, and immutability. However, its inherent transparency often conflicts with the need for user privacy and anonymity, raising significant concerns regarding accountability, especially in regulatory and legal contexts. This study explores the delicate balance between anonymity and accountability in blockchain systems, proposing a framework that ensures both privacy and compliance with regulatory requirements. The research addresses key challenges in balancing these two aspects, evaluates the effectiveness of existing privacy-preserving technologies such as zero-knowledge proofs and ring signatures, and introduces the Privacy-Accountability Balanced Blockchain (PABB) Framework. This framework integrates Selective De-Anonymization, Self-Sovereign Identity (SSI), and the Adaptive Privacy-Accountability Control (APAC) Algorithm to dynamically adjust privacy levels based on regulatory conditions. Through theoretical analysis, mathematical modeling, and empirical validation, preserving privacy for 92% of transactions while enabling selective de-anonymization in high-risk cases, the study demonstrates that the APAC Algorithm effectively balances privacy and compliance needs. The findings suggest that privacy-conscious blockchain systems can coexist with accountability mechanisms, paving the way for ethical and legally sound blockchain applications. The study concludes that the PABB Framework offers a practical and scalable approach to achieving this balance, fostering trust among users and regulators alike.
Behnam Khayer, Siamak Mirzaei, Hooman Alavizadeh, Ahmad Salehi Shahraki
Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem.
Jamila Tileubaevna Arzieva, Ali Tileubaevich Arziev, Nawrızbay Baxtiyar ul Seytniyazov, Ilham Kongratbay ulı Tlemisov
THE ROLE OF ZERO-KNOWLEDGE PROOFS IN ENHANCING CRYPTOGRAPHIC PROTOCOLS // Universum: технические науки : электрон. научн. журн. Arzieva J.T. [и др.]. 2025. 10(139). URL: https://7universum.com/ru/tech/archive/item/21052
Yair Rivera Julio, Ángel D. Pinto Mangones, Nelson A. Pérez-García, Mónica-Karel Huerta · 9 authors
Large-Language-Model (LLM) functionality is rapidly becoming a cornerstone of Telemedicine-as-a-Service (PGaaS) platforms. Recent Q1 studies demonstrate that even minuscule training-set or parameter perturbations can introduce persistent back-doors, while inference pipelines leak protected health information (PHI) if left unguarded. Building on the NIST AI Risk Management Framework (AI RMF), this paper proposes and implements a zero-trust, multi-cloud security architecture that couples (i) knowledge-graph–driven data-integrity validation, (ii) containerised fine-tuning isolation, (iii) AI-RMF–centred governance and continuous risk registers, (iv) a privacy-preserving response-sanitisation gateway enhanced with one-time-password (OTP) and KYC identity binding, and (v) remote-attestation-backed zero-knowledge-proof (ZKP) integrity challenges for model weights at runtime. An extensive multi-cloud evaluation shows that the framework detects 94.6 % of tainted samples before ingestion and blocks 91.3 % of unsafe outputs, with a median latency overhead of 66 ms—well below clinical tele-consultation thresholds.
Adversarial Robustness in Machine Learning
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Harsha Kumar A, Preetham Venkatram C, N. Saran, David Daniel · 5 authors
Traditional Electronic Health Record (EHR) systems suffer from critical vulnerabilities in security, interoperability, and patient data control. This paper introduces PolyMed, a novel decentralized platform designed to address these challenges. PolyMed combines blockchain, Artificial Intelligence (AI), and edge computing into a synergistic architecture. It uses the Polygon blockchain for immutable record-keeping and a Decentralized Autonomous Organization (DAO) for transparent governance. Patient identity is secured through privacy-preserving zero-knowledge proofs (ZKPs) and anchored to non-transferable Soulbound Tokens (SBTs), granting users true sovereignty over their data. The platform also includes a Decentralized Finance (DeFi) module to improve healthcare accessibility. Empirical evaluations on the Polygon Mainnet confirm the system's viability, showing sub-4-second transaction latencies and over 90% cost savings compared to legacy systems. The integrated AI model, leveraging a LightGBM classifier on a rich set of engineered features, achieves an Area Under the Curve (AUC) of 0.8543 and an accuracy of 80.33% in emergency detection, demonstrating high reliability on a clinically relevant and imbalanced dataset. By aligning with global standards like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), PolyMed offers an integrated platform for patient-centric digital health management.
This research presents a comprehensive solution and an attempt to prove the validity of the Collatz Conjecture, also known as the "3n + 1 problem," which has remained one of the most prominent open mathematical problems since 1937. The research presents two integrated methods for proving convergence, using advanced mathematical frameworks: 1. Summary of the First Solution Method (ملخص طريقة الحل الأولى) This method relies on integrating dynamical analysis, Hyperbolic Geometry, and p-adic number spaces. The study concluded by proving the following: * For every positive integer n, the repeated application of the Collatz function leads to the number 1. * There are no non-trivial periodic loops. * The number of steps required for convergence is O(\log^2 n). The proof is based on constructing a group of Contractive Transformations on a Hyperbolic Manifold, using a decreasing fractional energy function, and spectral analysis in the Z_2 space. 2. Summary of the Second Solution Method (ملخص طريقة الحل الثانية) This method offers a solution through a multidisciplinary framework that combines number theory, Topological Geometry, and Quantum Mechanics. Three essential pillars were developed to support the proof: * A Hybrid Energy Function. * An 8-Dimensional Topological Manifold. * A Quantum-Mathematical Verification System based on a quantum operator and a Zero-Knowledge Proof (ZKP) protocol. The researcher indicates that the entire content of the research was generated solely by Artificial Intelligence (AI) models under his supervision, emphasizing that these solutions are theoretical and derive their strength from a cognitive key discovered by the researcher, which enabled the AI to produce new scientific theories and solutions.
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the significant computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We show the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, traditional CRHs like SHA-2 or SHA-3, while optimized for CPUs, generate large circuits, rendering them inefficient in the ZK domain. Conversely, ZK-friendly hashes are designed for circuit efficiency but struggle on conventional hardware, often orders of magnitude slower than standard hashes due to their reliance on expensive finite field arithmetic. To bridge this performance gap, we present HashEmAll, a novel collection of FPGA-based realizations for three prominent ZK-friendly hashes: Griffin, Rescue-Prime, and Reinforced Concrete. Each offers distinct optimization pro les, with both area-optimized and latency-optimized variants available, allowing users to tailor hardware selection to specific application constraints regarding resource utilization and performance.Our extensive evaluation shows that latency-optimized HashEmAll designs outperform CPU implementations by at least 10×, with the leading design achieving a 23× speedup. These gains are coupled with lower power consumption and compatibility with accessible FPGAs. Importantly, the highly parallel and pipelined architecture of HashEmAll enables significantly better practical scaling than CPU-based approaches towards building real-world ZKP applications, such as data commitments with Merkle Trees, by mitigating the hashing bottleneck for large trees. This highlights the suitability of HashEmAll for real-world ZKP applications involving large-scale data authentication. We also highlight the ability to translate the HashEmAll methodology to various ZK-friendly hash functions and different field sizes.