Praveen Kumar Myakala, Anil Kumar Jonnalagadda, Sooraj George Thomas
This study introduces a novel intent-driven framework designed to advance privacy, scalability, and autonomous coordination in decentralized systems. Unlike traditional transaction-centric models, the proposed architecture allows users to express the desired outcomes through signed intents, enabling flexible and dynamic counterparty discovery without predefined execution paths. Solver-based optimization, leveraging constraint satisfaction techniques, matches, and composes these intents into valid transactions, facilitating complex workflows across heterogeneous trust and consensus environments. Composable privacy is achieved through integrated mechanisms, such as zero-knowledge proofs, homomorphic encryption, and programmable confidentiality, offering fine-grained control over data exposure and state transitions. The framework further supports multi-chain atomic settlement, dynamic rollup instantiation, and cross-domain interoperability, enabling applications across de-centralized finance, supply chain orchestration, and confidential governance. By unifying intent-centric interaction with modular security models, this architecture provides a scalable, privacy-preserving foundation for the next generation of decentralized analytics and autonomous decision-making systems.
Sarthak Gangurde, Ashwini Jadhav, Vijay Gatkal, Mansi More
The rapid advancements in genomics have raised concerns about the privacy of sensitive genetic data. This paper presents Zk-Gene, a Zero-Knowledge Proof (ZKP) framework designed for the secure verification of genetic markers. Zk-Gene ensures that genetic data can be verified without exposing sensitive information, maintaining user privacy while allowing for critical health assessments. We describe the architecture of Zk-Gene, the cryptographic techniques employed, the construction of ZKP circuits for genetic markers, and demonstrate its applicability to genetic testing scenarios. This framework provides an innovative method for safeguarding genetic privacy while enabling essential genetic verifications.
The security requirements of the metaverse are expected to surpass those of conventional methods as it develops into a completely immersive digital ecosystem. With its unmatched computational capacity, quantum computing presents both amazing possibilities and challenging difficulties for metaverse security. The revolutionary potential of quantum technology in creating cybersecurity frameworks specifically for the metaverse is examined in this chapter. We look at the weaknesses in conventional encryption in this future environment, the threats posed by quantum-based assaults, and the rise of quantum-resistant algorithms. This chapter explores significant advancements in distributed ledger technologies, zero-trust architectures, and quantum cryptography to provide a road map for creating a strong, durable metaverse infrastructure. Future-proofing the metaverse will depend heavily on maintaining security through quantum-enabled technologies as digital assets, identities, and interactions become more and more integrated into daily life. The goal of this conversation is to provide stakeholders, legislators, and technologists with the knowledge necessary to successfully negotiate the intersection of next-generation cybersecurity and quantum computing.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.
The surge in smartphone users has fueled the development of mobile Cloud Computing (MCC), a technology integrating cloud computing's power with mobile device mobility. MCC offers on-demand access to cloud resources like storage, processing power, and software, enhancing mobile device functionality and efficiency. This paper delves into user privacy concerns in 5G MCC environments. The convergence of mobile computing, wireless networks, and cloud computing has fostered the rapidly evolving field of MCC. Advancements in 5G mobile networks are expected to propel MCC services even further, making them a focal point in the mobile service landscape. However, 5G MCC also presents significant user privacy challenges, including network latency and data security. This paper investigates various cryptographic techniques employed to safeguard user privacy in 5G MCC systems. We compare and analyze encryption techniques, zero-knowledge proofs, MFA, and blockchain, and the inherent challenges associated with each approach.
Digital advancement is restructuring governance and electoral systems which significantly has increased the adoption of electronic voting (e-voting). E-voting has various advantages like being fast, easily accessible and being more transparent than ever. But it has some security concerns like manipulation, data breaches etc. One of the most lightening issues is the advancement of quantum computing. Various traditional protocols like the Elliptic Curve Cryptography (ECC) and RSA are prone towards quantum attack which causes e-voting to rely on classical techniques for authentication, confidentiality and integrity. To address these concerns, the concept of quantum-resistant decentralised voting mechanisms has emerged. These mechanisms integrate post-quantum cryptographic techniques to safeguard election integrity in a future where quantum computers may compromise existing cryptographic frameworks. Blockchain technology, homomorphic encryption and zero-knowledge proofs (ZKPs) collectively enhance security, verification process and voter privacy while mitigating the risks of quantum threats.
Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.
As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.
Beal's Conjecture (Andrew Beal, 1993) is a Clay Mathematics Institute Millennium Prize problem. It generalizes Fermat's Last Theorem: the exponential Diophantine equation Ax + By = Cz with positive integers A, B, C, x, y, z and x, y, z > 2 has integer solutions only if A, B, C share a common prime factor. This manuscript presents a conditional resolution via the Harmonic Coherence (HC) framework and Hanners Theorem (HT). We translate entropy-minimization principles from HC and HT into number theory. An entropy functional H(A,B,C) is defined over the normalized terms (Ax, By, Cz). Equilibrium (gradient zero) yields pi = 1/3, which requires Ax = By = Cz. Combined with Ax + By = Cz, this implies 2Cz = Cz—impossible for positive integers. Thus no coprime solution can satisfy equilibrium; any admissible solution must share a common prime factor. The proof is supported by modular arithmetic and congruence arguments (Stewart–Tijdeman, Darmon–Granville) and by extensive computational validation (34 tests, all PASS) over large integer domains. No counterexamples were found. v5.0 changes: Fixed L1 displacement bound in Lemma coprime-displacement from ≥ 1/6 to the correctly derived ≥ 1/3. Added perturbative translation note to the A3 closure strategy (bounded-height families as amplitude cutoff). Updated documentation (test count 15 → 34, Zenodo DOI). All changes sourced from deep vector DB mining of the knowledge system. Companion documents: • Contextual Entropy Reduction Theorem • Canonical Reconciliation (Song of Coherence) • HC Bridge Note • Fixed-Point Convergence Theorem • Paper A: Transformer Distillation as Spectral Filtering • Paper B: GW Kerr Ringdown • Paper C: HC Bridge Synthesis
Achieving a fair exchange between two mutually distrusting parties presents significant obstacles. The involvement of trusted third parties is often essential to ensure fairness in such exchanges. In this paper, we propose a blockchain-driven optimistic fair exchange protocol based on publicly verifiable secret sharing (PVSS) and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs). In our solution, arbiters, i.e., PVSS shareholders, serve as decentralized trusted third parties, registering on smart contracts with both autonomy and accountability. We assume a majority of arbiters are honest, ensuring that adversaries cannot successfully collude and that the fairness of the exchange protocol is upheld. In cases where both exchange parties are honest, the involvement of arbiters is unnecessary, making the fair exchange protocol optimistic. Additionally, we leverage zkSNARKs to generate non-interactive zero-knowledge (NIZK) proofs that ensure the correctness of the exchanged data. As a result, the exchange protocol is publicly verifiable without revealing any information about the exchanged data. In abnormal cases where arbiters are involved, a threshold number of them can fairly resolve the dispute between the seller and the buyer, and earn digital assets as a reward. In addition to fairness, optimism, and public verifiability, the proposed protocol also ensures completeness, termination, and privacy. Finally, we conduct experiments to illustrate the correctness and feasibility of the optimistic fair exchange protocol.
Jie Cui, Wenting Zhuang, Hong Zhong, Qingyang Zhang · 6 authors
The anonymity of blockchain may be exploited by criminals for illegal fund transfers, thus a conditional privacy-preserving scheme is important for blockchain regulation. Currently, sharding technology under a multi-chain architecture is used to improve blockchain scalability. However, current conditional privacy-preserving schemes cannot work on this architecture. To protect the privacy of the transaction, we present a conditional privacy-preserving transaction scheme (MC-CPPT) for multi-chain blockchain system. In this system, we proposed a zero-knowledge proof based anonymous transaction, in terms of the identities of transaction participants and amounts, which also enables the unlinkability of transactions and indistinguishability between cross-chain and intra-chain transactions in multi-chain blockchain system. In addition, a multi-node regulatory agency is introduced to control the transaction amount and frequency in the system without a single point of failure. Moreover, an ECC-based encryption scheme is proposed to achieve the traceability of suspicious transactions. A security model is defined and the security of MC-CPPT is demonstrated to meet the expected security goals. Evaluating the prototype revealed acceptable performance and additional security features.
As healthcare systems evolve and healthcare data grows, the need for cross-domain collaboration treatment has become more complex, necessitating fine-grained access control to enhance privacy and security. Blockchain provides a distributed trusted platform without third parties, but the current blockchain-based access control systems lack efficiency and sufficient privacy protection in cross-domain collaboration. To address these challenges, we propose SWIFTGUARD, an efficient and fine-grained access control system based on a master-slave chain to strengthen the security and privacy of cross-domain healthcare collaboration. SWIFTGUARD incorporates a zero-knowledge proof protocol for cross-domain authentication with-out exposing sensitive data and leverages quantitative attribute weights for efficient access control. Through game-based security proof, we demonstrate the zero knowledge and soundness of the system. Extensive experiments evaluate that SWIFTGUARD reduces the time complexity of access authorization from O($n$) to O(log$n$), with improved throughput and stable performance in cross-domain collaboration. Our comprehensive evaluation confirms that SWIFTGUARD provides a secure and efficient access control system for cross-domain healthcare collaboration.
The Internet of Things (IoT) connects numerous de-vices and sensors, generating data with significant informational and economic value. However, data silos hinder effective data utilization and trading, leading to the dispersion of data across various devices and systems. Additionally, traditional third-party trading models face challenges related to data security and trust. To address these issues, this paper proposes a secure data trading framework based on a consortium blockchain and designs a corresponding solution. Specifically, it introduces the integration of zero-knowledge proofs into the smart contract scheme for authenticity and integrity verification of transaction data. From the perspective of IoT device users, this paper aims to enable secure data trading through a decentralized platform, using off-chain storage methods to reduce the blockchain's data burden while ensuring security and privacy. Off-chain storage encrypts and securely stores sensitive data, recording only necessary information on the blockchain, effectively protecting user privacy. To validate the practicality of the proposed solution, experiments were conducted using Hyperledger Fabric, demonstrating its feasibility in facilitating secure storage and trustworthy trading of IoT data. Finally, this study analyzes the experimental results and offers valuable insights for future research.
Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. Cross-chain identity authentication is the primary pre-requisite of cross-chain security interaction. However, existing cross-chain identity authentication protocol generally has issues such as insufficient decentralization, poor universality, and low authentication efficiency. These issues compromise the reliability of cross-chain interactions. Based on group signature and zero-knowledge proof, this paper proposes a cross-chain identity authentication protocol to address these concerns. The protocol employs Decentralized Identifiers(DIDs) as the global identity identifier of users, utilizes zero-knowledge proof to provide privacy for the verification of users' identities when joining the group, and then constructs the users' transaction signatures through group signatures to hide the users' identities. This approach not only reduces the risk of users' privacy leakage but also facilitates mutual recognition of identities among cross-chain systems. Finally, the security analysis and experimental analysis shows the correctness and efficiency of the proposed scheme.
A growing interest in the Internet of Things (IoT) is sustained by recent developments in networking cloud, edge computing, and big data processing. In today’s data-driven digital economy, IoT data security is quickly rising to the top of the value chain as it enables the development of numerous business models that offer a wide range of intelligent and pervasive services. But, if a suitable privacy-preserving mechanism is not in place these data, which, include sensitive personal information, may reveal the identities of related stakeholders. Remarkably, blockchain-based methods support the distributed nature of IoT while providing robust countermeasures to prevent data from being corrupted. This paper includes research papers that focus on blockchain technology and its key characteristics for Edge Computing privacy preservation. The research articles are analyzed by grouping them according to the blockchain-based privacy-preserving methods for edge IoT that were used in earlier studies. Additionally, the problems and research gaps found in the previous works are enumerated so that future researchers can improve their work and provide a solution. Software tools, technique classification, blockchain technologies, and use cases are some of the factors that are taken into consideration when analyzing the works reviewed in the literature.ABBREVIATIONS: IoT: Internet of Things; FL: Federated Learning; ZKP: Zero-Knowledge Proof; SMPC: Secure Multi-Party Computation; DP: Differential Privacy; DL: Deep Learning; EHR: Electronic Health Repository; AI: Artificial Intelligence; LSTM: Long Short-Term Memory; KPABE: Key-Policy Attribute-Based Encryption; GRU: Gated Recurrent Units; HABE: Hierarchical Attribute-Based Encryption; APoW: Adaptive Proof of Work; IoMT: Internet of Medical Things; RSU: Roadside Units; BFT: Byzantine Fault Tolerant; RP: Radial Point; GM-SSO: Genetically Modified Salp Swarm Optimization; SVM: Support Vector Machines; TP2SF: Trustworthy Privacy-Preserving Secured Framework; PCA: Principal Component Analysis; ePoW: Enhanced Proof of Work; XGBoost: Gradient Tree Boosting System; PSI: Privacy Set Intersection; WSN: Wireless Sensor Networks; PoT: Proof of Trust; CPPA: Conditional Privacy-Preserving Authentication; BTCPS: Conditional Privacy-Preserving Announcement Scheme; VANET: Vehicular Ad Hoc Network; OMLIDS-PBIoT: Optimal Machine Learning – Based Intrusion Detection System ForProtecting Privacy In Biomedical Internet Of Things; GEO: Golden Eagle Optimization; FS: Feature Selection; RVFL: Random Vector Functional Link Network; HBO: Heap-Based Optimizer; HPT: Hierarchical Purpose Trees; TS-PBAC: Triple Subject Purpose-Based Access Control; MCS: Mobile Cloud Services; IoHT: Internet Of Health Things; DQN: Deep Q-Network; IECDSA: An Intelligent Elliptic Curve Digital Signature Algorithm; IIoT: Industrial Internet of Things; DPoS: Delegated Proof of Stake; RDPoS: Reputation-Based Delegated Proof Of Stake; MC: Manage Contract; TIC: Token Issue Contract; RNN: Recurrent Neural Network; NTRU: Nth-Degree Truncated Polynomial Ring Units; PKI: Public Key Infrastructure; PTAS: Privacy-Preserving Thin-Client Authentication Scheme; ML: Machine Learning; DNN: Deep Neural Network; PPSF-BODL : Privacy-Preserving Secure Framework Using Blockchain; With Optimal Deep Learning; URC: User Register Contract; ML: Machine Learning
The Zupply framework introduces an anonymous authentication protocol that utilizes zero-knowledge proofs to ensure data integrity, participant anonymity, and unlinkability within supply chains. Zupply employed the Groth16 zkSNARK, which requires a trusted setup. This paper explores the integration of Aurora, a transparent setup post-quantum secure zkSNARK, to the Zupply framework. This paper presents the core Zupply arithmetic circuits, including Auth, Trans, Merge, and Div, which allows succinct zero-knowledge proofs without exposing sensitive data. We evaluate the performance of the Zupply Aurora-based implementation across varying Merkle hash tree depths, comparing it to the original Groth16-based setup for BN254 and BLS12-381 elliptic curves.
Electronic health management systems (EHMS) play a crucial role in modern healthcare. They store important medical information such as patients’ medical histories, treatment plans, and diagnostic reports. However, the current EHMS landscape faces numerous challenges. These challenges hinder efficient data management, and collaboration among stakeholders, and compromise patient privacy and data security. Blockchain technology offers a promising solution to address these issues. By leveraging its decentralized and immutable nature, blockchain can create a secure, transparent, and tamper-resistant platform for storing, managing, and sharing electronic health records. This paper proposes an end-to-end solution called Healthchain based on a private permissioned blockchain network. Healthchain uses the Istanbul Byzantine fault tolerance (IBFT) consensus algorithm to reduce computation costs and increase speed. Secure multiparty computation allows medical data monetization without compromising privacy or data authenticity (ensured by blockchain) along with Zero knowledge proof and differential privacy for researchers. HealthChain also uses an efficient method for storing medical reports using IPFS, reducing the burden on single storage. It creates awareness about privacy issues and creates awareness about social engineering attacks based on the project. This paper explains the proposed framework, comparing performance with existing traditional and digital systems, and considers Bangladesh as an example to test its viability.
Abstract Blockchain technology offers a secure solution for managing sensitive data with Artificial Intelligence, supply chain, cloud computing, and healthcare applications. Its key features, confidentiality, decentralization, security, and privacy, enhance healthcare systems, especially when integrated with Internet of Things (IoT) devices. This integration improves communication between healthcare systems and IoT devices, increasing security, privacy, and operational efficiency. However, traditional healthcare systems face challenges like phishing, identity theft, and masquerading attacks. We propose a blockchain-based decentralized application to mitigate these risks and generate, maintain, and validate healthcare medical certificates. The application enables secure communication between hospitals, patients, doctors, and IoT devices using smart contracts for confidentiality and authentication. Our architecture utilizes Non-Interactive Zero-Knowledge Proof to maintain data integrity and privacy. We further integrate Blockchain Data Storage and the Inter-Planetary File System to reduce storage costs and enhance security through Ethereum smart contracts. An Intrusion Detection System monitors IoT traffic to detect potential security threats. Performance analysis demonstrates that this solution addresses key security and privacy challenges, offering an efficient and scalable framework for healthcare data management.
With the rise in need of Electronic Health Records (EHR), keeping confidentiality, integrity, and privacy intact of Patient Health Information (PHI) becomes of utmost importance, particularly while sharing via email which is inherently non-secure. The current work proposes a hybrid encryption approach on the basis of Advanced Encryption Standard (AES-256) with symmetric key cryptography and Elliptic Curve Cryptography (ECC) as asymmetric encryption. AES-256 is selected because it performs well to encrypt large datasets, and ECC provides secure key exchange and digital signature support with less computation overhead compared to RSA. The system addresses issues of key encryption, such as secure transmission of information, identification authentication, and digital signatures for message integrity.Besides AES and ECC, the system incorporates a Zero-Knowledge Proof (ZKP) protocol, enabling privacy-preserving identity authentication without exposing sensitive data. This is important in ensuring PHI protection because ZKP permits identity and message authenticity verification with private data left secure. The hybrid system is balanced between speed of encryption and security, such that it remains scalable and usable for real-time healthcare use cases. By integrating AES for quick data encryption, ECC for secure key management, and ZKP for added privacy, this solution provides an end-to-end solution for securely sending PHI through email. It successfully solves encryption issues while maintaining confidentiality, integrity, and privacy in healthcare communication without compromising efficiency. This encryption framework ensures compliance with major data privacy regulations including HIPAA and GDPR, making it applicable for real-world healthcare environments across both the U.S. and the EU.
Mobile payment systems have transformed financial transactions with unprecedented ease and accessibility. But the explosion in their numbers has been met with increasing security threats and issues of privacy. This paper puts forward a new blockchain-based paradigm for mobile payment systems that prioritizes privacy but retains high-security standards. This design leverages the essential capabilities of blockchain technology like decentralization, immutability, and cryptographic protection. It incorporates advanced privacy-protecting techniques such as zero-knowledge proofs and homomorphic encryption. The system design intended here is to mitigate common vulnerabilities present in centralized systems, reduce the risk of data breaches, and provide users with more control over their financial data. By comparative analysis and simulations, we show that our privacy-oriented blockchain solution strongly improves transaction security, minimizes fraud, and maintains user anonymity without sacrificing system performance or compliance with regulations. This work is a contribution to the ongoing work on developing more secure and privacy-aware financial technologies in a rapidly digitalizing economy.