Code-based group signatures are a promising candidate for post-quantum cryptography, but existing code-based group signature schemes struggle with the challenges of large signature sizes caused by zero-knowledge proofs. To address this issue, we propose a novel and practical code-based group signature scheme built upon the Schnorr-Lyubashevsky paradigm. Our construction achieves constant-size signatures and public keys, independent of the group cardinality, and its security is formally proven in the random oracle model under the hardness assumptions of the Syndrome Decoding (SD) and Decoding One Out of Many (DOOM) problems. To alleviate the performance bottleneck of rejection sampling, we design and implement a batch processing optimization for the signing algorithm, which significantly accelerates signature generation by applying vectorization to the most computationally intensive operations. Experimental results show that the optimization renders signing practical. Our scheme features the most compact signature size among existing codebased group signature schemes. All related code is open-sourced and available at https://github.com/Latters/CodeBasedGroupSig/.
Accounting is undergoing a radical transformation due to the integration of traditional information systems with blockchain technology and artificial intelligence. Openness, automation, and smart decision-making will all become a reality via this connection. However, traditional SAIS are typically centralized and do not inherently include blockchain or AI. In this study, Smart Accounting Information System (SAIS) technologies are redefined through the integration of these technologies to enhance transparency, automation, and real-time assurance. Blockchain technology's immutability, traceability, and AI's ability to recognize abnormalities and predict provide a more intelligent and secure auditing process. Conventional accounting methods have several issues, including delayed audits, lack of transparency, fraud, and human mistakes. Existing systems fail to provide intelligent anomaly detection and real-time transaction traceability. Financial reporting and audits need immutable records and proactive analytics. There is an urgent need for a single framework to ensure this requirement and its quick implementation. This study proposes the collaborative blockchain-AI audit trails method (CBAATM) for Smart Accounting Information Systems. This is done due to the difficulties mentioned. AI-powered modules utilize fuzzy inference to dynamically analyze audit risks and Random Forest classifiers to detect real-time fraud. This research project utilizes zero-knowledge proofs and homomorphic encryption to simultaneously handle data aggregation, privacy, and independent audits. Using middleware application programming interfaces makes integration with ERP and AIS systems easy. Throughout the testing process, the model outperforms conventional audits. The methodology, according to statistical research, ensures the detection accuracy ratio of 95%, integrity of the blockchain 99.2% of the time, identifies abnormalities 94.1% of the time, satisfies compliance standards 95.4% of the time, and reduces audit latency by 41.5% compared to other existing models.
Explainability is critical for human-AI decision-making, but methods centered on faithful and plausible explanations fail for complex Large Language Models (LLMs). This paper proposes a new paradigm that replaces the goal of model transparency with the goal of interaction verifiability. We frame human-LLM collaboration as a deliberation and introduce a formal Interactive Human-LLM Deliberation Protocol that incorporates human cognitive limitations. We then prove that this protocol functions as an Interactive Proof Protocol and can be extended to an Interactive Zero-Knowledge Proof (ZKP) Protocol. This framework shows that trust in LLMs can be established through rigorous, structured dialogue and recasts the human user's role as an "Effective Verifier" responsible for verifying and challenging the model's claims.
A zero-knowledge proof demonstrates that a fact (like that a Sudoku puzzle has a solution) is true while, counterintuitively, revealing nothing else (like what the solution actually is). This remarkable guarantee is extremely useful in cryptographic applications, but it comes at a cost. A classical impossibility result by Goldreich and Oren [J. Cryptol. ‘94] shows that zeroknowledge proofs must necessarily sacrifice basic properties of traditional mathematical proofs - namely perfect soundness (that no proof of a false statement exists) and non-interactivity (that a proof can be transmitted in a single message). Contrary to this impossibility, we show that zero-knowledge with perfect soundness and no interaction is effectively possible. We do so by defining and constructing a powerful new relaxation of zero-knowledge. Intuitively, while the classical zero-knowledge definition requires that an object called a simulator actually exists, our new definition only requires that one cannot rule out that a simulator exists (in a particular logical sense). Using this, we show that every falsifiable security property of (classical) zero-knowledge can be achieved with no interaction, no setup, and perfect soundness. This enables us to remove interaction and setup from (classical) zero-knowledge in essentially all of its applications in the literature, at the relatively mild cost that such applications now have security that is “game-based” instead of “simulation-based.” Our construction builds on the work of Kuykendall and Zhandry [TCC ‘20] and relies on two central, longstanding, and well-studied assumptions that we show are also necessary. The first is the existence of non-interactive witness indistinguishable proofs, which follows from standard assumptions in cryptography. The second is Krajíček and Pudlák’s 1989 conjecture that no optimal proof system exists. This is one of the main conjectures in the field of proof complexity and is the natural finitistic analogue of the impossibility of Hilbert’s second problem (and, hence, also Gödel’s incompleteness theorem). Our highlevel idea is to use these assumptions to construct a prover and verifier where no simulator exists, but the non-existence of a simulator is independent (in the logical sense of unprovability) of an arbitrarily strong logical system. One such logical system is the standard axioms of mathematics: ZFC.
This is a derivative of the German version that you can find here. Many modifications and improvements have been made in this version. The Gaia Economy – The VisionThis new economic and monetary system is a project for structural balance. It addresses the feelings and incentives of the wealthy, the middle class, and the poor alike. Critically, this new economic and monetary system makes it significantly easier to establish genuine social-democratic systems. Instead of allowing inequality to develop unchecked – which must then be corrected by taxing the rich – the Gaia Economy preventatively stops the accumulation and hoarding of wealth from the start. A central mechanism is demurrage (a circulation-maintenance fee): money does not need to be “recaptured” through taxes; instead, a continuous stream of funds is created by the natural decay of idle balances. Technically, this means: Treasury Accrual: Idle balances pay a small fee (e.g., 0.5% per month) into a transparent Treasury. Operations & Impact: This Treasury funds system operations (security, audits) and the Impact Layer. Separation: The Impact Layer decides allocations based on transparent, verifiable criteria, but it never gates or controls the Payment Layer. Status & ImplementationThis manuscript represents the first half of the complete work; further chapters detailing advanced implementations and global scaling are forthcoming. However, we are not waiting for the text to be finished to act. The Payment Layer and Impact Layer have already been programmed. They are fully functional and ready to use as an application. This app will be released officially alongside the implementation of the first pilot project. The Gaia Economy is conceived as a learning system – errors are data that can be changed through a rigorous governance process. We invite you to build, test, and improve with us. Collaboration requests, constructive criticism, and questions are highly welcome. Contact: info@dzydent.com Abstract: The Gaia Economy (U.S. Edition) The DiagnosisThe current monetary system contains a structural flaw: positive interest and compound interest automatically shift wealth upward, generating permanent pressure for growth and rationalization. This "invisible vacuum cleaner" siphons purchasing power from the real economy into financial asset hoards. The Solution: Two Separated Modules The Gaia Economy introduces a new economic infrastructure consisting of two deliberately separated layers: Payment Layer (Gaia Coin): A neutral, non-speculative payment rail. It anchors a light circulation pressure (demurrage) in code. This ensures money keeps flowing, making hoarding unattractive. It serves as a medium of exchange, not a wealth storage vehicle. It is non-custodial and privacy-preserving (no on-chain PII), utilizing zero-knowledge proofs (ZKPs) to validate transactions without disclosing personal details. Impact Layer (Voluntary Incentives): An optional layer that rewards verifiable contributions to the common good (e.g., ecological repair, care work, education). It operates on a cash-basis: rewards (Vouchers) are paid out of realized Treasury inflows, ensuring the system never creates debt or inflation. It evaluates entities, not individuals, preventing "social credit" surveillance. Governance & SafeguardsTo prevent capture, the Gaia Economy utilizes common-good councils and a multi-quorum governance system. Changes to core parameters require a supermajority and a mandatory timelock (delay), ensuring no rule changes happen overnight. Implementation StrategyIntroduction proceeds via closed-loop pilots (municipalities, universities, merchant associations) that run in parallel with the U.S. Dollar. The Gaia Economy is positioned as complementary infrastructure – compatible across political camps – secured through clear legal frameworks (e.g., 501(c)(3) stewardship, licensed partners for fiat ramps). Executive Summary (For Decision-Makers) Starting Point & GoalThe Gaia Economy responds to structural mis-incentives in the existing monetary system (hoarding, wealth concentration, growth pressure) with a practical, legally grounded alternative that runs voluntarily in parallel to the USD. Core Solution Gaia Coin (Payment Layer): A digital cash replacement with embedded demurrage to stimulate local circulation. Architecture: Energy-efficient consensus, pseudonymous wallets, open-source code. Neutrality: Payments are never gated by behavior or AI. Impact Layer (Incentives): A voluntary layer that rewards verifiable outcomes. Mechanism: Impact Vouchers are minted for verified actions and redeemed for Gaia Coin. Pacing: Payouts are strictly paced by the Budget_k (realized treasury inflow) to ensure solvency. Verification: Relies on off-chain evidence and Human-in-the-Loop review; AI is assistive only. Governance & Compliance (U.S. Context) Immutable Core: The separation of Payment/Impact and the prohibition of positive interest are unchangeable. Parameter Registry: Adjustable parameters (e.g., demurrage rate) require Supermajority + Timelock. Compliance: Pilots start as non-custodial closed loops. Any custody or fiat interaction is handled exclusively by licensed partners (banks/MTLs), ensuring compliance with U.S. regulations without burdening the protocol. Introduction & Scaling Phase 1 (Pilot): Private, closed-loop implementation with anchor merchants and a local nonprofit. Phase 2 (Regional): Integration with municipal services and licensed on/off-ramps. Phase 3 (Network): Inter-regional connection. Benefits Short term: Faster local circulation (Velocity), reduced merchant transaction costs, transparent funding for local projects. Mid term: Measurable strengthening of care, education, and environmental protection through the Impact Layer. Long term: A socially stable, ecologically compatible economy that relies on incentives rather than coercion. Immediate Next Steps The software and the blockchain currency are ready. The path forward is execution: Sign non-controlling MOUs with pilot partners (City/University). Define the Impact Catalog v1 (verifiable metrics for local needs). Deploy the Protocol v1.0 App (Wallet + POS + Treasury Dashboard). Establish Governance (GIP process, Council selection). Deploy Monitoring (Public Dashboards for Treasury and Impact KPIs). Keywords: Gaia Economy, Demurrage, Dual-Module Economic Architecture, Cash-Basis Budgeting, Impact Vouchers, Non-Custodial, DAO Governance, Social Democracy, Justice, Fair, Anti-Hoarding, Common Good, Sustainable Development.Work to be done next:Part X – Technical Blueprint & Pilot-to-Scale Roadmap (expanded, detailed, integration-ready) Part XI – The Mathematics of GAIA (Balance Equations) (10-point micro-structure per subsection) Part XII – Environment, Animals, Public Health (Special Topics) (10-point micro-structure per subsection) Part XIII – Practice: U.S. Case Studies (10-point micro-structure per subsection)
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
This interdependent network of vehicles has been made possible by the widespread adoption of self-driving cars operating as interconnected swarm networks based on continuous data exchange. Leading a Data-Driven Paradigm Shift However, these networks are not without their challenges, as they are prone to security threats, data privacy vulnerabilities, and inefficient consensus mechanisms to facilitate decentralized decision-making. This study proposes a novel blockchain architecture using AI that could offer enhanced security by utilizing a hybridized consensus protocol for autonomous vehicles. Accordingly, machine learning algorithm-based optimizations in blockchain consensus algorithms, such as PoW, PoS, and DPoS consensus methods, facilitate real-time adaptive adjustments, lower mining costs, and accelerated transaction validation, all within the framework of decentralized trustworthiness. In self-driving car ecosystems, AI-augmented security processes like anomaly detection, deep learning-based intrusion prevention, and federated learning, enhance threat detection while lowering the cybersecurity risks. Moreover, privacy-enhancing cryptographic methods, such as homomorphic encryption, zero-knowledge proofs (ZKPs), and differential privacy, are incorporated to safeguard sensitive vehicle information against unauthorized access while allowing compliance with data privacy laws. Experimental evaluations confirm that the proposed AI-empowered frameworks lead to improved system resilience, optimized resource allocation and improved transaction throughput and latency in contrast to traditional blockchain implementations. Overall, this study demonstrates that using AI-enabled blockchain models can provide a fundamental method for protecting and improving autonomous vehicle networks.
AI development requires reliable datasets, yet today’s data supply chains face challenges in traceability, authenticity and ethical compliance. This study introduces a blockchain-assisted data integrity framework that ensures transparent and verifiable provenance for AI model training. The proposed system uses smart contracts to record data lineage, ownership, preprocessing transformations and annotation events. IPFS-based off-chain storage reduces blockchain load while ensuring immutability. A verification engine allows auditors to evaluate dataset compliance with ethical and regulatory standards, including bias mitigation and consent validation. Experiments utilized three real AI workflows: medical imaging, sentiment analysis and environmental sensor classification. Findings show a 92 percent reduction in provenance disputes and an improvement in audit efficiency by 41 percent. The system also provides tamper-resistant documentation supporting responsible AI governance. Latency tests show minimal performance impact due to parallelized validation nodes. This research demonstrates that blockchain can provide a robust backbone for ethical AI ecosystems, where transparency and trust are critical. Future work will explore confidentiality enhancements using zero-knowledge proofs.
Hailong Zhang, Hong Zhao, Qiannan Chen, Chong Sun · 12 authors
Despite growing blockchain adoption in energy markets, its integration into critical operational functions — particularly relay protection— remains limited. Existing solutions fail to meet the stringent requirements of cryptographic auditability, cross-domain coordination, and configuration integrity in hierarchical grid architectures. To address this gap, we propose a three-tier blockchain-based information architecture for relay protection, featuring hybrid PoA+PBFT consensus (1.8 s finality), zero-knowledge proofs (ZKPs) for privacy-preserving setting verification, and smart contracts for tamper-evident logging. Evaluated on a Hyperledger Fabric v2.5 testbed across provincial, municipal, and substation tiers, the system achieves 40.2% faster hierarchical synchronization and 100% integrity of operation records. While unsuitable for sub-100 ms tripping, the architecture excels in non-real-time workflows such as setting management, fault archiving, and collaborative diagnostics— laying the foundation for secure, auditable, and interoperable protection systems.
Modern digital ecosystems rely heavily on Open Source Software (OSS), but maintaining license compliance is still a major and unsolved problem. Current approaches rely on either manual audits, which are expensive, sluggish, and prone to error, or automatic scanners, which frequently fail with dual or bespoke licenses. Businesses, entrepreneurs, and academic institutions are exposed to serious legal, financial, and reputational concerns as a result of this divide. This project suggests a multi-layered OSS License Verification Framework that incorporates human-in-the-loop learning, logical reasoning, evidence-based validation, provenance tracking, and cryptographic assurance in order to overcome these constraints. To establish technical ground truth, the system starts with SBOM and SPDX provenance data, builds an attestation graph, and uses binary inference and differential tracing. License requirements are represented as vectors of obligations, assessed using a constraint solver and validated using zero-knowledge proofs (zk-proofs) to give auditors reliable proof of compliance. A human oracle ensures adaptation to changing license ecosystems by resolving ambiguities and continuously enhancing the knowledge base. The suggested framework seeks to provide an end-to-end, intelligent, and auditable solution for OSS licensing compliance by fusing automation with verifiability and adaptability. The results will help a variety of stakeholders, such as businesses looking to reduce risk, startups seeking quicker innovation, and academic institutions using OSS responsibly, all of which will contribute to a more secure and reliable opensource ecosystem.
As artificial intelligence becomes deeply embedded in critical sectors like finance and medicine, we face a pressing challenge: how to guarantee its integrity. At the heart of this issue is a conflict between the proprietary nature of AI models, which are valuable assets, and the growing need for transparency in their operations. This paper lays out an architectural blueprint that resolves this tension by bringing together blockchain technology and Zero-Knowledge Proofs (ZKPs). We show how it's possible to verifiably confirm that an AI model has run correctly without exposing any of its confidential internal parameters. We walk through a simulation where a Deep Neural Network (DNN) produces an inference, and a ZKP is generated to prove the calculation used the legitimate model weights. This proof, along with the public data, is then recorded on a decentralized ledger, creating a permanent, auditable trail. A key part of our work is a comparison of two major ZKP technologies, zk-SNARKs and zk-STARKs, where we break down their respective trade-offs. Our simulation's effectiveness is demonstrated through resilience testing; it successfully identified and rejected 100% of fraudulent attempts, including both tampered outputs and counterfeit models. This demonstrates the architecture's efficiency in creating a provably secure and auditable trail, lighting a path toward genuinely trustworthy AI.
Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan
<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>
The complexity class Quantum Statistical Zero-Knowledge ($\mathsf{QSZK}$), introduced by Watrous (FOCS 2002) and later refined in Watrous (SICOMP, 2009), has the best known upper bound $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$, which was simplified following the inclusion $\mathsf{QIP(2)} \subseteq \mathsf{PSPACE}$ established in Jain, Upadhyay, and Watrous (FOCS 2009). Here, $\mathsf{QIP(2)}$ denotes the class of promise problems that admit two-message quantum interactive proof systems in which the honest prover is typically computationally unbounded, and $\text{co-}\mathsf{QIP(2)}$ denotes the complement of $\mathsf{QIP(2)}$. We slightly improve this upper bound to $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$ with a quantum linear-space honest prover. Specifically, the honest prover uses space linear in the size of the transcript of the original $\mathsf{QSZK}$ proof system. A similar improvement also applies to the upper bound for the non-interactive variant $\mathsf{NIQSZK}$. Our main techniques are algorithmic versions of the Holevo-Helstrom measurement and the Uhlmann transform, both implementable in quantum linear space, implying polynomial-time complexity in the state dimension, using the recent space-efficient quantum singular value transformation of Le Gall, Liu, and Wang (CC, to appear).
The need for strong cybersecurity frameworks in the healthcare industry has increased due to the Internet of Medical Things (IoMT) devices and electronic health records (EHRs) exponential growth. This paper presents HealthSentinel-ZKP, a novel framework that leverages federated neuro-symbolic deep learning and zero-knowledge blockchain to secure electronic health data. In contrast to earlier models, HealthSentinel-ZKP combines CNNs, Transformers, and symbolic reasoning for multi-perspective intrusion detection. Federated learning and zero-knowledge proof mechanisms protect data privacy. Using an immutable ZKPenhanced blockchain, the system guarantees GDPR-compliant auditing and has a dual-stream anomaly detection architecture. HealthSentinel-ZKP is a next-generation healthcare cybersecurity paradigm, as demonstrated by experimental results on benchmark datasets that demonstrate superior performance in zero-day attack detection and privacy preservation.
In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.
Vinoth Punniyamoorthy, Ashok Gadi Parthi, Mayilsamy Palanigounder, Ravi Kiran Kodali · 6 authors
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.
The article presents an analysis of the robustness of an authentication scheme based on zero watermarking. The study examines a two-factor authentication scheme that uses "knowledge of something" (a password) and "possession of something" (a digital RGB image) as its factors. The zero watermarking algorithm chosen is based on DWT and K-means transformations, with additional use of the Swish function. The analysis is conducted by considering the theoretical complexity of the algorithm assuming the adversary knows its parameters, such as the password, the hash of the password, the image, the reference watermark, the transformation result, and other parameters. Previous studies have shown a high theoretical robustness of the scheme, which relies on the complexity of the password and the dimensionality of the image. For large image sizes (512×512 pixels and above), a relatively high level of cryptographic resistance is achieved. However, this robustness is not formally proven, and the actual strength may be significantly lower due to the specifics of the images and transformations, which can introduce additional vulnerabilities. The algorithm is subject to a relatively high rate of collision, associated with digital image transformations and matrix multiplications, which weakens its resistance. Authentication schemes and zero watermarking algorithms require further research, formal proof of cryptographic properties, and methods for integration into access control systems, as they can provide a high level of authentication robustness in systems with high noise levels. Additionally, the convenience and low cost of such schemes give them an advantage over other authentication methods. The study provides recommendations for improving the potential characteristics of the algorithm.
Open access
Advanced Steganography and Watermarking Techniques
Yaxian Dong, Zijun Zhan, Daniel Mawunyo Doe, Zhu Han · 5 authors
In the project-oriented construction industry, recruiting qualified workers who can finish the required tasks in a limited time is important. However, the high turnover rate in the construction workforce poses a challenge in verifying applicant information, leading to potential issues like information falsification and inaccurate assessments due to information asymmetry. Additionally, the industry’s male-dominated nature may foster stereotype-based biases, particularly concerning sensitive attributes (e.g., gender). Such situations contribute to unfair competition among applicants. The construction industry is also experiencing new technologies like BIM, AI, and Blockchain. Their integration shows potential for automation, fairness, information security, and trustworthiness in recruitment. To build a diverse and competent workforce, we propose a decentralized digital resume-based job applicant screening and appraisal framework via BIM, AI, and Blockchain. First, we develop a blockchain job applicant data model that distinguishes between personal privacy data and work-related data for record and storage. A permissioned Blockchain is then designed to facilitate partial transparency for potential employers while ensuring the confidentiality of applicants’ sensitive information. Specifically, for personal privacy data, sensitive attributes (gender, race, etc.) are safeguarded via encryption, and data (address, etc.) about company preferences (the desired distance range from the company, etc.) is also secured while allowing for employer verification via Zero-Knowledge Proofs and smart contracts for information protection. Utilizing time-stamped authentication, applicants’ work history (reference network-based and performance-based information) remains immutable and is securely accessible by potential employers. Based on the validated applicant data and diverse company requirements, the digital resume is generated and customized for each position through smart contracts. For validation, a prototype system is developed with the data from LinkedIn. The results show its feasibility for trusted, fair, secure, and effective construction recruitment.
The rapid proliferation of Internet of Things (IoT) devices across various industries, including healthcare, smart cities, and industrial automation, has introduced significant security, authenticity, and traceability challenges within increasingly complex supply chains. Although existing approaches have utilised blockchain-based digital identity solutions to address some of these concerns, persistent issues of counterfeit products and inadequate lifecycle transparency highlight the need for more robust, hardware-anchored identification mechanisms. Our work presents a novel architecture that integrates Physically Unclonable Functions (PUFs) and blockchain-based Soulbound Tokens (SBTs) to establish secure and verifiable digital identities directly tied to the physical hardware of IoT devices. By employing cryptographic tools such as fuzzy extractors, Merkle trees, and zero-knowledge proofs, the proposed architecture ensures accurate lifecycle tracking through key operational stages, including manufacturing, procurement, provisioning, maintenance, and eventual disposal or recycling. Performance evaluations conducted on the Ethereum Sepolia testnet demonstrate reasonable computational overhead in terms of gas usage and transaction confirmation times. The findings reveal that this approach aligns with NIST Special Publication 800-161 guidelines, as well as emerging regulatory standards, notably the European Union’s Digital Product Passport initiative, and has significant implications for enhancing transparency, sustainability, and security across global IoT supply chains.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Nur Shahidah Mohammad, Md. Mafizur Rahman, Md. Abdur Razzaque
Federated Learning (FL) enables multiple entities to collaboratively train models without sharing sensitive data, but it faces critical privacy, security, and efficiency challenges in healthcare intrusion detection systems. These issues are intensified by adversarial attacks, non-IID data, and the need for real-time performance. Existing FL methods struggle with gradient inversion, model poisoning, Sybil attacks, and high computational overhead, limiting their effectiveness in secure and scalable healthcare applications. This work proposes the PrivacyPreserving Personalized Federated Learning Intrusion Detection in Healthcare applications (P3FL-HIDS), integrating Byzantinerobust aggregation, gradient masking, and Zero-Knowledge Proof based authentication. Key features include strong adversarial resilience, protection of privacy against gradient inversion, personalized model adaptation for heterogeneous data, and secure participant authentication. Additional contributions include a dual-network training approach, adaptive clustering for personalization, and optimized secure communication for real-time healthcare scenarios. Experimental results on a Brain Tumor magnetic resonance imaging (MRI) dataset show that P3FLHIDS outperforms state of the art works in terms of accuracy, resilience, and resistance.
Abstract Zero knowledge succinct non-interactive arguments of knowledge protocol (zk-SNARK) is an application oriented variant of zero knowledge proof, which enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities and high efficiency, it has been widely deployed in various blockchain based applications to provide privacy and scalability. While these applications place high demands on small proof size, fast verification and decentralization, currently available zk-SNARK with the shortest proof size and the fastest verification speed is in the common reference string (CRS) model, that is they require the trusted setup. After the pioneering results proposed by Bellare et al. in ASIACRYPT 2016, there have been lots of efforts to construct zk-SNARKs that satisfy subversion zero knowledge (S-ZK) and standard soundness from the zk-SNARK in the CRS model. These constructions could be regarded secure in the bare public key (BPK) model because that the equivalence between S-ZK in the CRS model, and uniform non-black-box zero knowledge in the BPK model has been proved by Abdolmaleki et al. in PKC 2020. Thus, compared to the CRS model, the BPK model better characterizes decentralized blockchain based application such as cryptocurrencies and anonymous credentials. In this study, by leveraging the power of random oracle (RO) model, we proposed the first publicly verifiable non-uniform ZK zk-SNARK scheme in the BPK model maintaining comparable efficiency with its conventional counterpart, which can also be compatible with the well-known transformation proposed by Bitansky et al. in TCC 2013 to obtain an efficient designated-verifier zk-SNARK. We achieve this goal by only adding a constant number of elements into the CRS, and using an unconventional but natural method to transform Groth’s zk-SNARK in EUROCRYPT 2016. In addition, we propose a new speed-up technique that provides a trade-off. Specifically, if a logarithmic number of elements are added into the CRS, according to different circuits, the CRS verification time in our construction could be approximately 9–23% shorter than that in the conventional counterpart.