A Study on Privacy-Preserving Scholarship Evaluation Based on Decentralized Identity and Zeroknowledge Proofs

The emergence of blockchain technology has revolutionized decentralized data management by offering robust alternatives to traditional centralized database systems. This paper provides a systematic and comprehensive review of blockchain-based distributed databases, highlighting key architectural transformations, core enabling technologies such as Merkle Trees, PBFT, and Zero-Knowledge Proofs, and comparing them with conventional distributed databases. Real-world implementations including Hyperledger Fabric, BigchainDB, and OrbitDB are analyzed to assess their scalability, interoperability, and security capabilities. The paper also explores intrinsic security mechanisms, performance bottlenecks, and regulatory challenges that affect adoption. Finally, it identifies open research questions and future directions necessary for building scalable, privacy-aware, and interoperable decentralized database ecosystems suitable for enterprise and multi-stakeholder environments. Keywords— Blockchain databases, consensus mechanisms, data integrity, decentralized systems, distributed ledger, Merkle trees, Zero-Knowledge Proofs

Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures.

Blockchain technology has emerged in e-business as a revolutionary force in the realm of cybersecurity, offering novel solutions to the escalating challenges posed by the ever-evolving modern technology landscape. This study delves new e-business into the critical determinants that drive the application of blockchain technology in enhancing cybersecurity. With the proliferation of digital assets and sensitive information, safeguarding data integrity, confidentiality, and accessibility has become paramount. The research employs a multidisciplinary approach in e-business, drawing from the fields of cybersecurity, blockchain technology, and data management. It examines the intricate interplay between blockchain's fundamental features and their direct impact on fortifying cybersecurity measures. Key determinants explored include: Decentralization: Blockchain's decentralized architecture reduces the vulnerability of a single point of failure, rendering it more resilient against cyberattacks. Immutable Ledger: The immutable ledger ensures the integrity of stored data, making it exceptionally challenging for malicious actors to tamper with information. Smart Contracts in E-Business: The automation capabilities of smart contracts enhance the enforcement of security protocols, bolstering the protection of digital assets. Cryptography: Robust cryptographic techniques underpin blockchain security, safeguarding data from unauthorized access. Transparency: The transparent nature of blockchain allows for real-time monitoring and auditing, facilitating the early detection of security breaches. Data Privacy: Blockchain's data privacy features, including permissioned networks and zero-knowledge proofs, address concerns surrounding privacy in cybersecurity. The study also investigates real-world applications and e-business of blockchain technology in cybersecurity across industries, from financial services to healthcare and supply chain management. It highlights successful case studies and identifies the challenges and limitations faced in implementing blockchain solutions. In conclusion, this research underscores the transformative potential of blockchain technology in e-business by fortifying cybersecurity measures. By examining the critical determinants that drive its application, organizations and policymakers can make informed decisions regarding the adoption of blockchain to enhance cybersecurity in the modern technology era. The study encourages further exploration and innovation in this dynamic and rapidly evolving field to secure our digital future.

Federated Learning Transformers for Personalized Text Generation in Privacy-Sensitive User Settings

A dual auditing protocol for fine-grained access control in the edge-cloud-based smart home

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.

After going through past research, it is observed that the security of data depends on five important key features such as authentication, data Integrity, mutual authentication between two parties, non-repudiation, and key exchange algorithm. Even if one of these features fail then it may breach security in real-world scenarios. The Diffie-Hellman Key Exchange (DHKE) algorithm allows securing one feature called key exchange in the cryptography field. However, this key exchange algorithm is unable to provide authentication. As a result, this key exchange scheme is prone to a hostile attack named as Man-In-The-Middle Attack (MITM). To solve this type of attack, a novel algorithm has been proposed called modification of the DHKE algorithm. In this algorithm, they claim that the MITM attack on classical DHKE can be prevented by applying the necessary functionalities like encryption and Zero-Knowledge Proof (ZKP). Although, after going through result analysis and working techniques, it is observed that the modified DHKE algorithm is still suffering from MITM attack. In this paper, the main objective is to develop an algorithm for finding out this type of security flaw in the existing modified DH algorithm which includes the concept of required authentication and encryption functions and able to perform the MITM attack on the existing modified DHKE algorithm.

Cloud computing provides a novel computing paradigm for enterprises to store programs and data in the Cloud in a transparent manner, which poses the challenge of security and privacy. In this paper, based on homomorphic cryptography and Zero-Knowledge Proof, we present a novel privacy-preserving scheme for Cloud publish/subscribe service, which achieve efficient privacy-preserving authentication, data integrity, and publish-subscribe confidentiality. The performance evaluation and security analysis demonstrate the practice and validity of the proposed scheme.

– The rapid proliferation of blockchain-based decentralized applications has introduced critical security challenges ranging from vulnerable smart contracts to privacy leakage in on-chain transactions. Existing tools address these challenges in isolation, leaving practitioners to integrate disparate solutions. OmniShield is a unified, open-source blockchain security platform that consolidates AI-powered smart contract vulnerability scanning, zero-knowledge proof (ZKP) private transfers, and a private QBFT consensus network into a single cohesive system. The scanner combines static pattern analysis, symbolic execution, and a Gemini-LLM reasoning layer to detect reentrancy, integer overflow, access-control flaws, and twelve other vulnerability classes with severity ratings. Private transfers leverage Groth16 zk-SNARKs over Circom circuits so balances remain hidden on-chain while cryptographic validity is enforced. The underlying network runs on Hyperledger Besu with QBFT consensus, providing Byzantine-fault-toleran block production. Experimental results show the scanner correctly identifies known vulnerabilities in benchmark contracts, ZKP proof generation completes in under 15 seconds on consumer hardware, and end-to-end private transfers finalize within two consensus rounds. OmniShield demonstrates that enterprise-grade blockchain security can be packaged as an accessible, developer-friendly platform. Key Words: blockchain security, smart contract analysis, zero-knowledge proofs, zk-SNARKs, Hyperledger Besu, QBFT consensus, AI vulnerability scanner, reentrancy, Solidity.

Genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. In this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (ZKPs), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. We implemented and evaluated a proof-of-concept prototype in Python on a simulated genomic dataset. The prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated InterPlanetary File System (IPFS). Rule-based access is controlled using smart contracts, while privacy and security are achieved using ZKPs with interactive Schnorr protocol and elliptic curve cryptography (ECC). Empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (ZKPB) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ECC latency of 8.72 ms with 90.00% accuracy. These empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows.

The healthcare industry faces significant challenges in safeguarding data security and integrity, particularly with the rise of digital health systems and the sensitive nature of patient information. Blockchain technology offers a decentralized, immutable, and secure solution to address these issues, allowing healthcare organizations to protect and manage patient data effectively. This paper examines how blockchain can enhance data security and integrity within healthcare by examining its key features, such as immutability, transparency, and decentralized access, which collectively provide a robust framework for secure data management. The potential applications of blockchain in healthcare, including patient consent management, access control through smart contracts, and data-sharing solutions, are analyzed alongside challenges such as regulatory compliance, cost, and interoperability. By integrating blockchain, healthcare systems can achieve improved data security, fostering trust and resilience in patient data management. This paper concludes with case studies and discusses future trends in blockchain implementation within healthcare, underscoring its transformative potential for securing data while addressing privacy and ethical concerns. Keywords: Blockchain, healthcare, data security, data integrity, patient data management, smart contracts.

The development of a robust, transparent and interoperable E-healthcare infrastructure has been a difficult task due to many regulations and legislatures like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). Healthcare service providers prefer to store data about their patients in locked up silos, behind often inadequate layers of security and firewalls. Such an approach results in data breaches and limits the ability to get a holistic view of the medical history of a patient. The obscure cost of treatment is another issue brought to attention recently in media. In this paper, we have proposed a national blockchain framework for managing patients' Electronic Health Records (EHRs) access control and funds in the context of India's National healthcare scheme. We introduce a transparent insurance claim process for healthcare providers and an auditable trail of EHR access using smart contracts. We use a smart card approach allowing beneficiaries to authenticate their identity using zero-knowledge proofs and delegate access to service the providers via proxy re-encryption.

Biometric data, such as fingerprints, facial recognition, retina scans, and voice scans, constitute unique and irreplaceable forms of personal identification. In the digital era, biometric data is increasingly used in electronic security systems for identity verification and user authentication. However, despite these benefits, there are potential risks of privacy violations and data misuse, which impact human rights. This study aims to analyze the legal protection of biometric data in digital security systems from a criminal law perspective and examine criminal liability in the event of biometric data leaks or misuse. The research method used is a normative juridical approach, examining relevant statutory provisions, such as Law Number 27 of 2022 concerning Personal Data Protection, Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE), and the Criminal Code (KUHP). The results show that although biometric data has been recognized as legally protected personal data, criminal law enforcement mechanisms for biometric data-related violations are still suboptimal. This is due to the lack of clear technical regulations and limitations regarding the definition, storage, and use of biometric data by electronic system administrators. This study concludes that strengthening criminal law regulations and implementing policies is necessary to ensure comprehensive biometric data protection, including imposing strict sanctions on perpetrators of misuse and increasing corporate responsibility in maintaining the security of the public’s digital data.

With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This paper introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.

Participants can complete tasks on crowdsensing platform with smart mobile devices. They go to specific locations to collect data and upload them. Then, the platform pays them some rewards. However, most of the existing crowdsensing platforms are deployed on centralized servers, which are vulnerable to attack, intrusion, and manipulation. In addition, the location information in task records increases the potential risk of privacy leakage. To reach a balance between privacy protection and functional availability of the system. We design and implement Crowdchain, which is a location preserve anonymous payment system based on a permissioned blockchain. In order to disassociate participants identity information from task records, Crowdchain contains a novel protocol that makes participants transfer wallet address to payment system bypass the crowdsensing platform and receive task rewards correctly. We prove the security of our anonymous payment system by zero-knowledge proof and give the analysis result of system stability.