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.
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.
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.
Driven by globalization and digitization, the Mobile Industrial Supply Chain Internet of Things (IoT) has gradually developed, utilizing mobile devices and IoT technologies to enable real-time monitoring and efficient responses across various stages. However, with the growing demand for high-frequency data exchange, the Mobile Industrial Supply Chain IoT faces significant challenges in data security, authentication, and privacy protection. This paper proposes a security authentication scheme based on blockchain and group key management, leveraging the decentralized and tamper-resistant features of blockchain, the privacy-preserving authentication method of Zero-Knowledge Proofs (ZKP), and a hierarchical key management mechanism based on binary key trees. This approach aims to enhance the security and scalability of Mobile Industrial Supply Chain IoT. The experimental section simulates scenarios such as dynamic node addition and key updates, evaluating the performance in terms of encryption, decryption, and key management efficiency, thus demonstrating its superiority in multi-party collaborative environments.
Suhail Adel Alansary, Sarah M. Ayyad, Fatma M. Talaat, Mahmoud M. Saafan
Abstract The rise of artificial intelligence (AI) revolutionized both cybersecurity defenses and cybercriminals' methods to exploit vulnerabilities. Cybercriminals continue to exploit previously undiscovered vulnerabilities, known as zero-day attacks, posing severe threats to cybersecurity. These attacks are particularly challenging to detect, as they target unknown weaknesses in systems before security teams can respond or act. Traditional intrusion detection systems (IDS) rely heavily on pre-existing attack signatures, making them ineffective against zero-day threats. Machine learning (ML) algorithms have recently become a promising solution for enhancing IDS capabilities by identifying anomalies and predicting potential vulnerabilities in real time. This review paper explores how cutting-edge AI techniques, specifically ML, DL, and federated learning (FL), are harnessed to counter zero-day attacks. AI is used to defend against cyberattacks that exploit vulnerabilities unknown to existing security software. This research explores different AI methods used in cybersecurity, analyzes the data used to train these AI models, and evaluates how well various algorithms perform in actual cyberattacks. Moreover, key challenges in deploying ML for zero-day detection are highlighted, including handling imbalanced data, generalization across diverse types of attacks, and the trade-offs between accuracy and computational cost. The paper outlines future research directions to enhance AI-based zero-day attack defenses and strengthen proactive cybersecurity strategies.
Naser Abbas Hussein, Jihene Khoualdi, Ilhem Abdelhedi Abdelmoula, Hella Kaffel Ben Ayed
Internet of Things (IoT) has gripped domains with this ubiquitous connectivity, in-themoment data collection, and autonomous decision-making. But rising numbers of heterogeneous, extremely constrained IoT devices pose serious concerns regarding data privacy, security, and trust management, drawing great attention into these areas in the academic field and on all sides. Thus, blockchain technology came into the limelight for strengthening security and privacy in IoT systems in a decentralized manner, giving the system immutability, transparency, and distributed trust. This study proposes a Systematic Literature Review (SLR) of blockchain-based approaches that aim to enhance the IoT applications' privacy and security, focusing chiefly on healthcare, supply chains, and smart cities. The review uses a structured methodology to find, select, evaluate, and synthesize relevant peer-reviewed studies published between 2018 and 2025 taken from major scientific databases such as IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Articles were also examined to narrow the scope of study and set the subject. The selected studies are analyzed and classified based on their security goals (e.g., confidentiality, integrity, authentication), privacy-preserving techniques (e.g., anonymization, differential privacy, zero-knowledge proofs), blockchain configurations (e.g., public, private, consortium), and consensus mechanisms. The findings reveal a growing body of research applying blockchain to a wide range of IoT domains, addressing diverse application domains such as healthcare, smart homes, industrial IoT, and agriculture, and demonstrating its potential to enhance data integrity, access control, and authentication. However, the integration of blockchain in IoT also faces challenges such as scalability, latency, and resource overhead, especially in real-time and constrained environments. This review offers a comprehensive synthesis of the state-of-the-art, identifies current limitations and research gaps, and proposes future research directions for building secure, efficient, privacy-aware, and scalable blockchain-enabled IoT systems.
Financial decisions in production systems must satisfy a layered set of obligations: risk tolerance, regulatory compliance, fairness constraints, privacy requirements, and operational service levels.Most machine learning models optimize predictive objectives but treat policy and compliance as external checks.This separation creates avoidable failure modes: decisions that are accurate yet non-compliant, long audit cycles, and limited customer recourse.This paper proposes Policy-Carrying Decision Models (PCDMs): decision systems that emit not only an outcome (approve/decline/route) and calibrated confidence, but also a machine-checkable proof that the decision adhered to an explicit policy expressed in a domain-specific language (FinPol).At inference time, the model (and its surrounding decision logic) produces a decision receipt containing the outcome, explanations scoped to permissible disclosure, and a verifiable policy proof.Optionally, a zero-knowledge variant allows third parties to verify compliance without access to sensitive features or thresholds.
Abdullah Ayub Khan, Asif Ali Laghari, Hamad Al-Mansour, Leila Jamel · 8 authors
The multimedia environment has undergone significant growth, particularly in the area of multimedia data and its migration to cloud platforms, which has raised issues about security, confidentiality, data integrity, and privacy protection. While Blockchain Distributed Ledger Technology (BDLT) offers decentralized trust and transparency the advent of Quantum Computing threatens classical cryptographic primitives, which make multimedia data increasingly vulnerable. This paper proposes a novel and secure framework that collaborates BDLT with quantum-resilient, mainly known post-quantum cryptographic schemes to ensure long-term data integrity and privacy preservation in cloud-based infrastructures. Due to this, the proposed solution enables secure, efficient, and transparent that helps in public auditing of multimedia content without compromising stakeholder confidentiality. It leverages Zero-Knowledge Proofs (ZKPs), lattice-based cryptography, and smart contract automation, which model fortifies data authenticity verification against quantum attacks. Simulation results illustrate the effectiveness of the proposed framework that achieves a 98.21% accuracy in data integrity verification, a 96.84% reduction in quantum vulnerability, and an 87.85% efficiency gain in auditing speed compared to classical BDLT-enabled platforms. In addition, privacy leakage in multimedia systems is reduced by 92.47% proving the framework’s robustness. This solution underscores the potential of synergizing BDLT, quantum secure cryptography, and cloud computing to build a future-proof solution for privacy-protected multimedia data management and public auditing.
Open access
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques
V. Ananthakrishna, Bajrang Lal, Chandra Shekhar Yadav
The increasing demand for security and privacy-preserving collaboration among healthcare institutions presents significant challenges in data sharing, consent enforcement, and diagnostic automation, especially considering emerging quantum threats. This paper introduces PQ-FedCare, an innovative federated system architecture that incorporates post-quantum cryptography, zero-knowledge proofs, and smart contract–governed diagnostics to facilitate verifiable and privacy-compliant clinical collaboration. The proposed framework supports decentralized identity validation, encrypted consent delegation, and encrypted rule execution across blockchain-connected healthcare nodes. Using CRYSTALS-Kyber and SPHINCS+ for quantum-resistant security and zk-SNARKs for proof generation, PQ-FedCare ensures zero data exposure while enabling real-time, cross-institutional medical decision support. Evaluation on real-world clinical datasets (MIMIC-III, TCGA, and GEO GSE12102) demonstrates superior performance over recent baselines in diagnostic accuracy (94.5%), privacy leakage (0%), and proof verification time (92 ms). Additional stress tests confirm the system’s robustness against missing data and scalability across federated nodes. The findings establish PQ-FedCare as a forward-compatible infrastructure for secure, accountable, and future-proof federated healthcare diagnostics. The proposed work is particularly suited for high-stakes clinical environments demanding transparency, regulatory compliance, and resistance to quantum-era attacks.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralized protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledge–compatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymization entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
Dan Ivanov, Tristan Freiberg, Shahabi, Shirin, Jonathan Gold · 5 authors
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
Open access
2 source records
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This work presents a cryptographic protocol for secure multi-party verification that achieves com putational privacy while maintaining exceptional computational efficiency. The proposed Position Based Commitment Protocol (PBCP) introduces a position-dependent nonce mechanism combined with cyclic verification architecture, enabling se cure computation over private inputs without re vealing individual parameters. Unlike existing commitment schemes that require complex cryp tographic assumptions, computationally expensive zero-knowledge proofs, or extensive public key in frastructure, Fundamental innovation lies in adapt ing physical laws of fluid dynamics to create nat ural mathematical relationships where each verifi cation equation contains multiple unknowns, mak ing parameter extraction computationally infeasible while preserving verification integrity. The proto col preliminary analysis suggests O(n) communica tion complexity with O(n2) verification complexity, providing substantial improvements over traditional Byzantine Agreement protocols that require O(n3) message exchanges. Comprehensive security analysis reveals robust resistance against statistical attacks with complexity O(R3) where R represents the pa rameter range, complete immunity to timing attacks through blind submission mechanisms, and resilience against collusion attacks involving up to n/2 − 1 ad versarial parties. The protocol’s unique cyclic neigh bor verification creates an interdependent validation network that prevents individual parameter extrac tion while maintaining system-wide integrity through mathematical interdependence rather than crypto graphic assumptions.
In an era marked by increasingly sophisticated cyber threats and growing vulnerabilities in national critical infrastructure, this study explores the transformative role of confidential computing in defending against emerging cryptographic attacks and enabling secure threat intelligence sharing. Traditional cybersecurity measures, while effective for protecting data at rest and in transit, fall short in securing data during active processingan area exploited by advanced persistent threats, quantum computing, and side-channel attacks. This research investigates how hardware-based trusted execution environments (TEEs), homomorphic encryption, and zero-knowledge proofs embedded in confidential-computing platforms can preserve the confidentiality of sensitive operations even within potentially compromised environments. Through detailed case studies of major U.S. institutionsincluding PGandE, Exelon, JPMorgan Chase, Wells Fargo, and Kaiser Permanentethe paper demonstrates significant improvements in detection speed, false positive reduction, and operational efficiency. Furthermore, it proposes a scalable, privacy-preserving framework for collaborative cyber defense across critical sectors such as energy, finance, and healthcare. The findings underscore that integrating confidential computing with decentralized intelligence sharing networks not only enhances cybersecurity resilience but also yields substantial economic and regulatory benefits. This work advocates for a national, and eventually global, shift toward confidential-computing-enabled infrastructures to achieve robust, cooperative, and future-proof cyber defense ecosystems.
Academic publishing, integral to knowledge dissemination and scientific advancement, increasingly faces threats from unethical practices such as unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest. While existing infrastructures like ORCID effectively disambiguate researcher identities, they fall short in enforcing explicit authorship consent, accurately verifying contributor roles, and robustly detecting conflicts of interest during peer review. To address these shortcomings, this paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology. The proposed model uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to securely verify author identities and contributions, reducing ambiguity and ensuring accurate attribution. A blockchain-based trust registry records authorship consent and peer-review activity immutably. Privacy-preserving cryptographic techniques, especially Zero-Knowledge Proofs (ZKPs), support conflict-of-interest detection without revealing sensitive data. Verified authorship metadata and consent records are embedded in publications, increasing transparency. A stakeholder survey of researchers, editors, and reviewers suggests the framework improves ethical compliance and confidence in scholarly communication. This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
Blockchain technology has emerged as a promising solution for improving traceability across global supply chains, offering tamper-proof records and increased transparency.However, concerns related to data privacy, confidentiality, and interoperability continue to hinder widespread adoption.This paper proposes a comprehensive framework addressing these key challenges by combining privacy-preserving techniques-such as permissioned ledgers, zero-knowledge proofs, and verifiable credentials-with industry-driven data standards (GS1 EPCIS, W3C Verifiable Credentials).We first review the landscape of blockchain traceability solutions and outline critical requirements from regulatory and operational perspectives.Next, we detail our proposed privacy-preserving and interoperable architecture, incorporating off-chain storage, role-based permissions, and selective disclosure mechanisms to accommodate the diverse needs of modern supply chains.We illustrate these concepts through a high-level system design, accompanied by implementation considerations.Our evaluation highlights that successful adoption depends on carefully balancing transparency and confidentiality, supplemented by robust governance structures and standard APIs.The paper concludes by discussing future directions for blockchain traceability, emphasizing scalability, user-centric design, and cross-chain interoperability as critical enablers of a global, privacypreserving supply chain ecosystem.
Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.
The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.
With the widespread adoption of Internet of Things (IoT) technologies in healthcare systems, security issues related to user privacy during data transmission and sharing have become increasingly prominent. To address these challenges, this paper proposes a medical privacy protection and secure sharing scheme based on Quantum Key Distribution (QKD). The scheme integrates multiple technologies, including blockchain, smart contracts, zero-knowledge proofs, and Chebyshev chaotic mapping, to ensure secure data sharing and access control among multiple communication entities. Compared with existing solutions, our approach enhances key management security through quantum keys and improves communication resilience against attacks by leveraging chaotic systems. User identity privacy is protected via zero-knowledge proofs. Under the random oracle model, the security of the proposed scheme is formally proven. Moreover, comparative experiments with existing protocols demonstrate the scheme's comprehensive advantages in terms of security and performance, evaluated across throughput, computational overhead, communication overhead, and storage overhead.
Digital banking fraud has grown into a multifaceted threat requiring both strong cryptographic protections and advanced machine learning defenses. This paper proposes a unified framework integrating lattice-based cryptography (for post-quantum resilience and privacy) with federated graph neural networks (for collaborative fraud detection) to address the gap in current financial security architectures. We simulate real-world fraud scenarios –including synthetic identity schemes and transaction laundering – using a mix of publicly reported incidents (e.g., the 2023 Log4Shell exploitation, 2022 SolarWinds-style supply chain compromise, and the Mirai botnet’s IoT spread) as motivating cases. Our methodology leverages threshold homomorphic encryption for privacy-preserving analytics, along with adversarial training and diffusion purification to harden models against poisoning and evasion attacks. Experiments using a mixed dataset of EU banking transactions and simulated breach logs show that our approach achieves over 99% accuracy in detecting novel fraud patterns, while reducing false positives by 35% compared to conventional classifiers. We validate these improvements with statistical significance (p<0.001) and illustrate them via ROC curves and network topology maps. Key findings include the identification of specific trade-offs between cryptographic overhead and detection latency, and the observation that cross-institutional intelligence sharing (using zero-knowledge proofs) can halve the response time to coordinated attacks. These results suggest that combining cryptography with machine learning can close existing vulnerabilities in digital banking and guide future work in robust, privacy-aware fraud prevention.
This review article examines the state of blockchain-enabled identity management in Internet of Things (IoT) networks, focusing on decentralized and secure mechanisms for device identification, authentication, and access control. Traditional centralized identity systems face limitations such as single points of failure, scalability bottlenecks, and vulnerability to breaches. We systematically survey recent literature on blockchain-based frameworks applied to IoT, categorizing approaches by blockchain platform, identity credential models, consensus mechanisms, and smart contract implementations. The analysis highlights key performance metrics such as system latency, throughput, resource overhead, and energy consumption, and compares existing prototypes deployed across diverse IoT scenarios. We assess the security and privacy implications, including resistance to spoofing, Sybil attacks, unauthorized access, data tampering, and insider threats. Additionally, the review identifies open research challenges such as managing identity lifecycle in constrained devices, achieving interoperability across heterogeneous networks, balancing decentralization with scalability, and integrating with emerging technologies like edge computing and zero-knowledge proofs. Finally, we offer recommendations for future research directions and practical deployment strategies to advance blockchain-based identity solutions in IoT ecosystems. Our comprehensive synthesis aims to guide researchers and practitioners in developing robust, scalable, and trustworthy identity frameworks using blockchain for the evolving IoT landscape.
This study introduces a framework that integrates blockchain, decentralized identity, and zero-knowledge proofs to enhance the trustworthiness and confidentiality of disaster information sharing. A sustainable model is proposed for real-world applications, supported by a prototype developed on the Decentralized Solutions for Humanity (DS4H) blockchain research network.
Emanuela Podda, Pol Hölzmer, Alexandre Amard, Johannes Sedlmeir · 5 authors
Zero-knowledge proofs allow the implementation of the data minimisation principle imposed by the GDPR in digital identity wallets and the related personal data transactions, therefore representing a reasonable option to be enforced by lawmakers.