With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.
Zeineb Ben Sassi, Chiheb Chahine Yaici, Jiahui Xiang, Osman Salem · 5 authors
Zero-Knowledge Proof (ZKP) protocols offer a powerful foundation for privacy-preserving authentication by allowing one party to prove knowledge of a secret without revealing it. Such protocols are increasingly relevant in domains such as secure communications, blockchain technologies, digital identity management, and e-health, where data confidentiality and integrity are critical. While various ZKP schemes exist, their practical performance remains a key factor in choosing the appropriate protocol for real-world applications, since efficiency directly impacts scalability, user experience, and system adoption.In this work, we conduct a comparative benchmarking study of five no table ZKP-based authentication protocols: Fiat–Shamir, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK), Zero-Knowledge Scalable Transparent Argument of Knowledge, (zk-STARK), Schnorr, and Guillou–Quisquater. Each protocol is evaluated in a standardized virtualized environment to ensure fair comparisons across implementations. We measure and analyze multiple performance metrics, including prover and verifier execution time, Central Processing Unit (CPU) and memory consumption, and network usage per proof. Our results reveal significant differences in resource efficiency, highlighting trade-offs between computational cost, proof size, and cryptographic expressiveness.This study provides a systematic evaluation clarifying the relative strengths and weaknesses of widely used ZKP protocols, serving as a practical reference for researchers, practitioners, and system designers seeking to integrate zero-knowledge techniques under real-world performance constraints.
Kriti Patidar, Swapnil Jain, Mohammad Husain, Mohd Muqeem · 9 authors
As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
In a time of growing environmental issues and climate change, the drive toward sustainability is more important than ever. Startups and small businesses are expected to be more instrumental in forming a sustainable future as world economies move toward greener paradigms. For many of these businesses, though, the financial load related to sustainable infrastructure, eco-innovation, and clean technology still be a major obstacle. For sustainable businesses trying to bring environmentally friendly ideas to market without sacrificing financial viability, green financing options including grants, subsidies, and green loans provide essential lifelines. Emphasizing the need of access to specific funding resources that support environmentally friendly practices, this abstract investigates the several green financing options open to startups. Examining both public and private sector projects emphasizes how green finance closes the innovation gap with implementation, especially for early-stage businesses trying to scale their green solutions. Grants and subsidies represent among the most well-known sources of green money. Usually governments, international organizations, and environmental NGOs supply these financial support to inspire creativity in fields including waste management, green manufacturing, sustainable agriculture, and renewable energy. Grants are a great choice for startups with limited cash flow since they unlike loans do not demand repayment. Many environmental grantinitiatives to support clean tech development have been started in areas including the European Union, North America, and portions of Asia. As part of the EU's larger goal to reach net-zero emissions by 2050, the European Green Deal, for instance, provides billions in support to sustainable businesses. To lower the initial costs of green investments, numerous local and national governments also provide direct subsidies and tax breaks. These could include financing for research and development of low-carbon technologies, subsidies for fleets of electric vehicles, or rebates for solar panel installations. In addition to fostering the growth of green startups, these policies hasten the market uptake of sustainable goods and services. Green loans have become a powerful instrument for sustainable finance in addition to grants. These are loans specifically designated for environmentally beneficial projects, and they frequently have favorable conditions like reduced interest rates, extended payback periods, or repayment plans that are based on performance. To assist with climate-resilient projects, organizations such as the World Bank, the Green Climate Fund, and several green investment banks provide specialized green loan programs. In order to specifically serve small and medium-sized businesses (SMEs) with environmental missions, some commercial banks have also entered this market by introducing green loan portfolios. Accessing green loans or grants for startups in need of these funds necessitates both a strong business plan and an unambiguous proof of environmental impact. The majority of funding organizations assess applications using standards like energy efficiency, circularity, social sustainability, and carbon footprint reduction. Thus, it is essential to have solid environmental metrics and data to support assertions. Furthermore, obtaining certifications such as B-Corp status or compliance with ESG (Environmental, Social, and Governance) standards can boost one's credibility and chances of getting funding. Additionally, startups now have more opportunities to interact with mission-driven investors who value sustainability in addition to financial returns thanks to the growth of impact investing. Green-minded venture capital firms and angel investors frequently offer seed money to eco-innovative companies, seeking high-growth prospects in line with long-term environmental objectives. Additionally, by reaching out to eco-aware communities, crowdfunding websites such as Kickstarter and Indiegogo are being used to fund green startups. Notwithstanding these encouraging advancements, obstacles still exist. Many startups are not equipped with the knowledge, skills, or resources necessary to successfully negotiate the intricate world of green finance. Grant and loan application procedures may be extremely competitive and cumbersome. Additionally, global scalability is hampered by the uneven distribution of green funding across various regions. Governments, financial institutions, and the private sector must work together more closely to close these gaps in addition to implementing policy changes and raising entrepreneur financial literacy. To address these challenges, startup incubators, accelerators, and advisory organizations are increasingly offering green finance consulting services, helping early-stage companies identify suitable funding options, prepare compelling applications, and build investor-ready sustainability strategies. Digital tools and platforms are also emerging to match green startups with appropriate funding sources, thereby streamlining the connection between innovative ideas and capital. In conclusion, green financing is not merely a niche category of economic support; it is an essential enabler of the global transition toward a more sustainable economy. By making green finance more accessible, equitable, and aligned with the realities of early-stage startups, stakeholders can unlock a wave of innovation that tackles some of the world’ s most pressing environmental issues. Whether through grants, subsidies, green loans, or impact investing, the opportunities for sustainable entrepreneurship have never been more abundant, but seizing them requires a well-informed, strategic, and purpose-driven approach.
Aug 24, 2025·Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, 2026
Thomas Gassmann, Stefanos Chaliasos, Thodoris Sotiropoulos, Zhendong Su
Zero-knowledge proofs (ZKPs) are the cornerstone of programmable cryptography. They enable (1) privacy-preserving and verifiable computation across blockchains, and (2) an expanding range of off-chain applications such as credential schemes. Zero-knowledge virtual machines (zkVMs) lower the barrier by turning ZKPs into a drop-in backend for standard compilation pipelines. This lets developers write proof-generating programs in conventional languages (e.g., Rust or C++) instead of hand-crafting arithmetic circuits. However, these VMs inherit compiler infrastructures tuned for traditional architectures rather than for proof systems. In particular, standard compiler optimizations assume features that are absent in zkVMs, including cache locality, branch prediction, or instruction-level parallelism. Therefore, their impact on proof generation is questionable. We present the first systematic study of the impact of compiler optimizations on zkVMs. We evaluate 64 LLVM passes, six standard optimization levels, and an unoptimized baseline across 58 benchmarks on two RISC-V-based zkVMs (RISC Zero and SP1). While standard LLVM optimization levels do improve zkVM performance (over 40\%), their impact is far smaller than on traditional CPUs, since their decisions rely on hardware features rather than proof constraints. Guided by a fine-grained pass-level analysis, we~\emph{slightly} refine a small set of LLVM passes to be zkVM-aware, improving zkVM execution time by up to 45\% (average +4.6\% on RISC Zero, +1\% on SP1) and achieving consistent proving-time gains. Our work highlights the potential of compiler-level optimizations for zkVM performance and opens new direction for zkVM-specific passes, backends, and superoptimizers.
Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li · 5 authors
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.
Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound implications in many domains, including machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment because of their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a (growing) body of researchers has proposed hardware accelerators and GPU implementations of both kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that vary significantly in computational overhead, proof size, verifier cost, protocol setup, and trust. The latest and widely used ZKP protocols are intentionally designed to balance these trade-offs. One particular challenge in modern ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator to efficiently handle arbitrary custom gates via SumCheck. Our accelerator achieves upwards of $1000\times$ geomean speedup over CPU-based SumChecks across a range of gate types. We include this unit in zkPHIRE, a programmable, full-system accelerator that accelerates the HyperPlonk protocol. zkPHIRE achieves $1486\times$ geomean speedup over CPU and $11.87\times$ geomean speedup over the state-of-the-art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of $2^{30}$ constraints) and maintaining small proof sizes ($4-5$ KB).
Sai Teja Reddy Adapala, Yashwanth Reddy Alugubelly
The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.
Harsh Yadav, Varun Shukla, Hamdan Zaman khan, Anshul Kumar Mandal
The currently existing Aadhaar data sharing system has a dearth of granular user control enabling other third party entities including banks, telecom service providers, and government agencies access to the user's data without explicit, time bound and purpose specific consent. This incites all manner of privacy, transparency, and data misuse concerns. This paper then proposes a Blockchain Based Consent as a Service (CaaS) Framework to guarantee such real time, secure and auditable access of Aadhaar linked data in the hands of users via authorized smart contracts. In the proposed model, the smart contract encapsulates each data access request includes purpose, requesting entity, the type of data, and the duration of access. A secure app or web interface will notify user or browse regarding consent requests, and allow him to accept or refuse request by using Aadhaar Virtual ID and OTP or digital signatures. Once it is approved, the access is granted to consumers on a temporary basis with full logging on a blockchain ledger that is immutable and open for all to see by users and regulators. It is compliant with the Digital Personal Data Protection (DPDP) Act 2023 and utilizes off chain storage of encrypted data. In addition to allowing users to possess granular control over their digital identity, the framework also lays the groundwork for Zero Knowledge Proof (ZKP) based functionalities and AI based anomaly detection to proactively protect users' privacy in the future.
Securing sensitive physical and digital areas, such as equipment rooms, medical records storage, and intensive care units (ICUs), is crucial in modern health care environments. Traditional ways of access control that depend on static authorization and centrally maintained databases are becoming more vulnerable to insider threats, identity spoofing, and data breaches. To enhance privacy, transparency and realtime threat detection in healthcare infrastructure, paper suggests a conceptual architecture for a secure, decentralized access control system that integrates blockchain technology, biometric authentication, and Zero-Knowledge Proofs (ZKPs). Recognition of fingerprints serves as the system's main authentication technique, and feature vectors are safely stored on a decentralized blockchain and cryptographically committed using Pedersen commitments. A zk-SNARK is generated during access requests to verify the accuracy of the user's biometric input without disclosing the real biometric data. Smart contracts validate access decisions, allowing for unaltered event logging and automated policy enforcement. The system combines entry-point security with Edge AI-based continuous monitoring, which tracks people's movements within the secure area using motion sensors and CCTV. The individual's continued authorization during their presence is guaranteed by periodic behavioral verification conducted by ZKPs. Anomalies that are discovered are immediately reported and stored on the blockchain for forensic examination. The approach suggested combines behavioral confirmation with physical identity verification to provide a strong multifactor authentication (MFA) framework. Although conceptual in nature, the architecture provides a scalable and privacypreserving model for next-generation healthcare access control systems because it is based on blockchain and cryptography technologies that have been proven to work.
Blockchain-based systems ensures data immutability and traceability, making them well suited for decentralized content governance. However, conventional metadata anchoring strategies remain static and task-agnostic, resulting in suboptimal trade-offs between auditability, privacy, and scalability. To address this limitation, we propose a Task-Driven Dynamic Metadata Mapping (TDMM) mechanism that adapts anchoring strategies based on task semantics and user roles. TDMM classifies metadata into three distinct anchoring types: full on-chain anchoring for audit-critical tasks, selective disclosure via zero-knowledge proofs (ZKPs) for sensitive attributes, and off-chain reference anchoring for general-purpose metadata. A dedicated mapping controller dynamically routes metadata fields to the appropriate anchoring mode according to predefined task policies. To preserve privacy without sacrificing verifiability, TDMM incorporates a hybrid ZKP architecture that processes proofs off-chain while anchoring verification results on-chain. We implement TDMM on a Hyperledger Fabric network, augmented with IPFS and Circom-based ZKP tooling. Experimental results show that TDMM significantly reduces on-chain storage overhead, lowers re-identification risk, and supports task-appropriate latency and throughput trade-offs, demonstrating its effectiveness in balancing transparency, privacy, and scalability in decentralized metadata governance.
Abstract This chapter explores the transformative role of AI across various dimensions of healthcare, highlighting the interplay between analytical and generative AI in medical imaging, as well as the implications of generative AI in drug discovery and development. It reviews generative AI in the broader framework of P4 medicine—predictive, preventive, personalized, and participatory—and the potential of AI to improve health outcomes, while tackling the rising costs and increased demand for healthcare professionals in aging populations. The requirement for multimodal longitudinal datasets is examined, as well as the challenges around data sparsity and data bias, and the need for data equity. The chapter further evaluates inherent risks and the most relevant aspects of regulation relating to the use of AI in healthcare, personal data protection, and security. In conclusion, the chapter reviews the potential future impact of agentic AI, and technology convergence with robotics, zero-knowledge proofs and blockchain, and quantum computing.
Artificial Intelligence in Healthcare and Education
Hesam Azadjou, Suraj Chakravarthi Raja, Ali Marjaninejad, Francisco J. Valero‐Cuevas
Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 refinement trials (each lasting 20 seconds) to achieve specific cyclical movements. This process mirrors the exploration-exploitation paradigm observed in mammals. With each refinement, the robot progressively improves upon its initial "good enough" solution. Our results serve as a proof-of-concept, demonstrating the hardware-in-the-loop system's ability to learn the control of a tendon-driven quadruped with redundancies in just a few minutes to achieve functional and adaptive cyclical non-convex movements. By advancing autonomous control in robotic locomotion, our approach paves the way for robots capable of dynamically adjusting to new environments, ensuring sustained adaptability and performance.
Range arguments are a type of zero-knowledge proofs that aim to prove that a prover's committed value falls within a specified range for a verifier. Previously, most range arguments were constructed based on the DLOG assumption, and hence, exponentiation operation is required for proof generation and verification. In addition, it is generally known that splitting a zero-knowledge proof protocol into a preprocessing phase and an online phase makes computation after fixing the input efficient. Still, such protocol has yet to be known for range arguments. This paper proposes an efficient range arguments protocol with a preprocessing phase. Our proposal takes a new approach by using arithmetic circuits to express the constraints that the prover must prove. The prover (resp. verifier) can generate (resp. verify) a part of proof based on multiplication and addition operations instead of exponentiation operations. Our range argument is a generic construction that does not rely on any particular mathematical assumptions, which enables us to construct a post-quantum range argument. The implementation evaluation shows that the total computation time for the prover and verifier in the online phase is efficient compared to Bulletproofs, one of the state-of-the-art range proofs. Especially, the prover computation is efficient.
Securing and protecting privacy of patient records are crucial challenge in block chain. In this paper, proposed Secure Hybrid Framework for Blockchain(SHFB) is confronted by integrating AES-GCM and ECC with Zero Knowledge Proofs and Fully Homomorphic Encryption (FHE) to enhance the security efficiency. The exponential growth of healthcare data necessitates secure and efficient frameworks to handle real time processing and protection against cyber threats. This paper presents the SHFB (Confronted) model, a block chain integrated architecture optimized for high throughput, robust denial of service (DoS) resistance, and low latency performance in electronic health record (EHR) systems. Comparative analysis reveals that SHFB surpasses conventional models such as Decentralized Security, AI Privacy and Secure EHR in key metrics, making it a scalable and secure solution for modern healthcare infrastructure. Patient records were used to conduct experimental evaluation and demonstrate that SHFB significantly reached high to compare with existing models. Particularly SHFB achieves an encryption time of 0.02 seconds, security efficiency of 95%.The proposed method shows the effectiveness of hybrid method by enhancing the performance and security of block chain in management of data using healthcare.
This paper presents a comprehensive framework for deploying a blockchain-based electronic voting system in Rwanda to address challenges of transparency, security, and public trust in electoral processes. Through detailed analysis of the current Rwandan electoral infrastructure and limitations, we propose a multilayered blockchain architecture that incorporates advanced cryptographic techniques, a national digital identity framework, and mobile accessibility features tailored to Rwanda's unique socio-economic landscape. Our proposed system leverages permissioned blockchain technology with a hybrid consensus mechanism to ensure the immutability of vote records while maintaining voter privacy through zero-knowledge proofs. The paper further discusses implementation challenges specific to Rwanda's context, including digital literacy (UNESCO, 2019), infrastructure limitations, and regulatory considerations. Our findings suggest that progressive, phased implementation of blockchain voting systems can significantly enhance electoral integrity while maintaining cultural and technological accessibility for Rwanda's diverse population.
Ana-Maria Istrate, Fausto Milletarì, Fabrizio Castrotorres, Jakub M. Tomczak · 7 authors
Abstract Reasoning models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. In open domains like biology, however, we lack exact rules to enable large-scale formal verification and instead often rely on lab experiments to test predictions. Such experiments are slow, costly, and cannot scale with computation. In this work, we show that world models of biology or other prior knowledge can serve as approximate oracles for soft verification , allowing reasoning systems to be trained without additional experimental data. We present two paradigms of training models with approximate verifiers: RLEMF : reinforcement learning with experimental model feedback and RLPK : reinforcement learning from prior knowledge. Using these paradigms, we introduce rbio1 , a reasoning model for biology post-trained from a pretrained LLM with reinforcement learning, using learned biological models for verification during training. We demonstrate that soft verification can distill biological world models into rbio1 , enabling it to achieve state-of-the-art performance on perturbation prediction in the PerturbQA benchmark. We further show that composing multiple AI-verifiers improves performance and that models trained with soft biological rewards transfer zero-shot to cross-domain tasks such as disease-state prediction. We present rbio1 as a proof of concept that predictions from biological models can train powerful reasoning systems using simulations rather than experimental data, offering a new paradigm for model training.
R. Praveen Kumar, G. Gautham Kumar, Arun Amaithi Rajan, V. Vetriselvi · 5 authors
Remote sensing and satellite imaging have become essential in various geological and surveillance applications. These systems often rely on cloud platforms for storing satellite and aerial images, introducing trust and security concerns, especially in sensitive domains like border surveillance, monitoring, and reconnaissance. Traditional cloud solutions are prone to data breaches and adversarial attacks, highlighting the need for a secure, end-to-end framework. To address this, we propose a comprehensive security architecture for storing and retrieving sensitive remote sensing images. Our system ensures confidentiality, integrity, and access control, while also resisting adversarial attacks during image retrieval. It adopts a three-phase structure: secure authentication, secure storage, and secure retrieval. Authentication is achieved using a combination of Zero-Knowledge Proof and Quantum Key Distribution, establishing a tamper-proof user verification process. In the storage phase, quantum-based cryptography secures the images, while a deep hashing model resistant to adversarial attacks enables efficient indexing and retrieval. A watermark embedding mechanism helps detect insider threats and support forensic tracking in case of data breaches. Evaluated on a remote sensing image dataset using various backbone networks, our system achieved a retrieval accuracy of 94.77%, outperforming existing models by 8–12%. The framework is well-suited for high-security environments, including military applications.
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques
Supply chain finance (SCF) plays a key role in easing financing difficulties for small and medium-sized enterprises, but it also comes with risks such as information asymmetry, fraud involving pledged assets, and delays in credit evaluation.In this study, we introduce a dynamic risk management framework driven by IoT and enhanced by the integration of multiple technologies.Built on a four-layer IoT structure, comprising perception, network, processing, and application layers, the framework combines blockchain for secure and trusted data sharing, federated learning for collaborative data processing, and digital twin models for real-time risk simulation.At the perception level, 5th-Generation Mobile Communication Technology (5G)enabled low-power sensors ensure comprehensive and tamper-proof data collection.The network layer uses blockchain techniques such as sharding and zero-knowledge proofs to safeguard data privacy and institutional trust.In the processing layer, federated learning combined with edge and cloud computing enhances credit evaluation.On the other hand, the application layer employs smart contracts and feedback mechanisms to enable real-time responses and adaptive risk strategies.To put this framework into practice, we propose a phased approach: first building a real-time data ecosystem, then deploying secure risk control systems, optimizing distributed computing, and finally integrating a closed-loop risk control mechanism.This modular, collaborative strategy ensures that technological systems align with actual business needs.Ultimately, the research demonstrates how IoT, blockchain, and AI can work together to create a scalable and practical model for managing risk dynamically in SCF.
This article proposes a novel blockchain-based architecture for cross-border payments that integrates self-sovereign identity (SSI) and zero-knowledge proofs (ZKPs) to address the fundamental challenges of traditional systems. The proposed framework enables near-instant settlement while preserving privacy and ensuring regulatory compliance by design. By layering an identity infrastructure with ZKP-gated smart-contract escrows and regulatory oracles, the system allows participants to prove compliance with jurisdiction-specific requirements without revealing sensitive personal data. The architecture comprises three interconnected layers — identity, value, and compliance — that work together to streamline remittances, business transactions, and international payroll processes. Comparative analysis demonstrates significant advantages over both correspondent banking and current blockchain networks in terms of settlement speed, transaction costs, fraud prevention, and automated compliance. While the approach faces challenges, including network adoption barriers, technical scalability, and governance complexity, this study outlines promising directions for future development, particularly in the context of emerging central bank digital currencies (CBDCs) and regulated stablecoins.