Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Thi Hong Tran
Traditional digital identity systems struggle with centralization, vulnerability to manipulation, and a lack of transparency. In distributed identity, different cryptographic methods are used for issuing credentials, that create challenges during presentation. It suffer from a fundamental interoperability barrier with heterogeneous digital-signature schemes, forcing each verifier either to implement every scheme or to trust a central translation gateway. We propose a signature-agnostic verification framework that eliminates this barrier. The core idea is to commit a salted root hash of credential claims to a distributed ledger and ensure the authenticity using a smart contract. A zero-knowledge proof (zk-SNARK) is used to prove a selected claim set without revealing actual information. The verification reduces to a single hash-consistency check, and the verifier never touches issuer-specific signatures. A pleasant side effect is that the same verifiable presentation (VP) can be reused across verifiers and sessions, since trust derives from the on-chain anchor rather than transient signatures. This research will advance the identification ecosystem, enabling applications such as eKYC across finance, healthcare, and other sectors. We implement our method on Ethereum Virtual Machine (EVM) using Groth16, benchmark gas cost, proof size, and latency, and show its feasibility and computational efficiency. The privacy and security analysis confirms that the proposed solution is resistant to various attacks.
V. S. Ramachandran, Hayder M. A. Ghanimi, Bhaskar Marapelli, Navleen Kaur · 8 authors
This paper introduces a Cyber-Physical System (CPS) that can be used to improve privacy protection in enterprise Blockchain (BC) systems, particularly in Hyperledger Fabric (HF). The proposed CPS employs advanced methods to facilitate a privacy-preserving authorization mechanism. Using data masking, Homomorphic Encryption (HE), and complex digital signatures, the model ensures the confidentiality of transaction data during the authorization process. Additionally, the implementation of Secure Multiparty Computation (SMC) and Zero-Knowledge Proofs (ZKP) enhances the security of the data by preventing unauthorized personnel from accessing it and ensuring that the approving peers remain anonymous. The solution proposed in the paper addresses the problems of conventional centralized access control, which can be manipulated and has data leakage problems. Experimental results have proved the design’s practical applicability and security trade-off, providing a robust foundation for enterprises to adopt privacy-preserving BC. Thus, the HF platform integration demonstrates the model’s real-world applicability, which developed a secure, scalable, and efficient solution for handling sensitive transactions in distributed networks.
Natalia Borgoñós García, María Hernández Padilla, Antonio Fernando Skarmeta Gómez
Abstract Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.
Secure personal data sharing remains a critical challenge in decentralized systems due to concerns over privacy, compliance, and trust. This paper presents the formal verification of a Blockchain-Based Security Model (BSM) designed to address these challenges through a multi-layered architecture. The proposed model integrates Chaincode-as-a-Service (CCaaS) on Hyperledger Fabric to ensure modular, maintainable, and scalable execution of smart contracts. A Flask-based API serves as the secure gateway for data operations and identity management. Sensitive data is stored off-chain using InterPlanetary File System (IPFS), preserving decentralization while minimizing on-chain bloat. Access control is enforced using efficient cryptographic techniques, while Intel SGX (or simulated enclaves) safeguards secure data processing and decryption within trusted execution environments. To further enhance privacy guarantees, Zero-Knowledge Proofs (ZKPs) are optionally integrated to enable verifiable claims without disclosing raw data. For assurance of correctness and security, the BSM is formally modeled using the Dolev-Yao attacker model and verified through ProVerif, focusing on key security properties such as confidentiality, integrity, authentication, and accountability. The findings confirm that the proposed model satisfies stringent security goals and is robust against symbolic adversaries. This work contributes a verifiable and extensible framework for privacy-preserving data sharing in sectors such as healthcare, finance, and government. To the best of our knowledge, this is among the first works to formally verify a blockchain-based security model that simultaneously integrates modular chaincode execution (CCaaS), trusted hardware enclaves (Intel SGX), decentralized off-chain storage (IPFS), and optional Zero-Knowledge Proofs (ZKPs) with a unified framework for personal data sharing.
Luigi Pavarini de Lima, Liliam Sayuri Sakamoto, Jair Minoro Abe, Jonatas Santos De Souza · 8 authors
The objective of this article is to propose a research structure to optimize this security of assets with NFT - Non- fungible Token with the use of DLP - Data Loss Prevention and Paraconsistent Logic for the identification not only preventively, but actively of the loss, theft, misuse and leakage of this type of assets during their use in the Metaverse. A bibliographic review was carried out on Metaverse, DLP, Paraconsistent Logic, Artificial Intelligence techniques [10][27][28], NFT [49][50], and Data Protection [4] with a focus on the LGPD (Brazilian Data Protection Law) [2][23], in conjunction with exploratory research. With a DLP and a database provided by the transport company with 200 articles analyzed. It was verified that a significant amount of data would be discarded in the first stage of the process (37%) since they do not present an active definition on the status of these assets. Considering the growing technological innovation with the use of the Metaverse, as an environment for educational, business and governmental interaction against the risk of cyber-attacks, there is an urgent need to strengthen its security, even more so when in this environment, where there is the possibility of moving assets with NFTs that are objects of great value acquired and traded in this medium. With the use of the Python program in the DLP, it was observed that it presented a 37% data loss in its analysis with this Artificial Intelligence [11] process only with the performance of the DLP, compared to the optimization of this analysis with the use of Paraconsistent Logic at 23%, that is, a use of more than 15% of the data.
Laura García, Carlos Cancimance, Rafael Asorey-Cacheda, Claudia Liliana Zúñiga Cañón · 6 authors
Data integrity and traceability are important challenges to provide security in the Internet of Things (IoT) networks, which are often vulnerable to data manipulation attacks due to their use of low-resource devices and wireless communication technologies. In this regard, blockchain is a promising solution to enhance IoT security, but the implementation of a conventional blockchain requires high computational and network connectivity resources that are not compatible with IoT networks. In this paper, we propose a lightweight blockchain for data integrity and traceability in IoT networks that adapts the Distributed Ledger Technology (DLT) feature of blockchain to the LoRaWAN wireless communication protocol. Our proposal offers data integrity without the need for complex consensus algorithms or cryptographic operations.We also have designed and implemented a logical LoRaWAN P2P topology that enables communication between the IoT nodes which comprise LoRaWAN’s characteristic star topology. Finally, we evaluate our proposal and demonstrate its feasibility and performance in terms of data traceability, and network overhead.
ABSTRACT An increasing number of knowledge resources are stored and disseminated in digital form, resulting in new challenges for Intellectual Property Rights (IPR) protection, including difficulty in establishing rights, difficulty in defending rights, and difficulty in incurring high costs. The proposed system in this paper aims to use Internet of Things (IoT) devices to collect knowledge resource data, store it in the Interplanetary File System (IPFS) network, and mint non‐fungible tokens (NFTs) in the blockchain, simplifying the process of IPR confirmation and protection while reducing costs. Additionally, blockchain transactions are delivered to the blockchain service network (BSN) to enhance network credibility. Experimental results show an average file storage time of 0.05 s, a 13% reduction in the average time for property rights registration, and a reduction in maintenance costs.
Mazharul Hasan, Mohammad Jabed Morshed Chowdhury, Kamanashis Biswas, Mahmudul Hasan · 5 authors
Non-Fungible Tokens (NFTs) have revolutionized the blockchain ecosystem by enabling the decentralized representation and ownership of unique digital assets. However, the NFT landscape is increasingly susceptible to security vulnerabilities and attack vectors, necessitating robust protection mechanisms. This research presents a comprehensive analysis and a structured framework to enhance the security of NFT transactions. The relevant studies were identified using a snowballing method as the paper searching approach, ensuring systematic and comprehensive coverage of the existing literature. Specifically, it examines critical security risks, including ownership duplication, duplicate token generation, unauthorized asset withdrawal, asset replication, and metadata manipulation. A key contribution of this study is the development of a detailed taxonomy of NFT-related threats and attacks, providing researchers and practitioners with a consolidated perspective on the evolving security landscape. Furthermore, we introduce and analyze attack vectors, demonstrating its implications and proposing an effective mitigation strategy.
Open access
Blockchain Technology Applications and Security
Cloud Data Security Solutions
Physical Unclonable Functions (PUFs) and Hardware Security
Alejandro Hernán Son Romero, Nicolás Xavier Herrera Medina, Pablo Alberto Rojas Jaén
This article introduces a hybrid blockchain architecture to enhance Electronic Medical Record (EMR) management and interoperability. It integrates a permissioned public blockchain on Polkadot—managing roles and permissions—with a private blockchain on Hyperledger Fabric responsible for EMR storage. Text data are stored in CouchDB and medical images in IPFS as Non-Fungible Tokens (NFTs), following a patient-centric model. Stress tests yielded average latencies of 2050 ms for EMR creation and 2000 ms for sharing, with 65 % CPU and 170 MB memory usage, indicating system stability and efficiency. The proposed architecture provides a scalable, secure, and interoperable solution suitable for healthcare environments that demand data confidentiality and controlled access.
The rise of blockchain and Digital Ledger Technology (DLT) has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and its consensus mechanism protect blockchain data from being tampered by unauthorized adversaries. However, implementing a full-fledged blockchain system to protect a database can be technically cumbersome. In this work, we introduce an in-database design, named chain table, to protect data integrity without the need for a blockchain system. It features a succinct design without significant technology barriers or storage overhead. To realize rigorous data security, we also propose a set of data writing principles for the chain table. We prove that the chain table, together with the data writing principles, will guarantee flexible data integrity, named table-level data integrity (TDI).
Lingyan Xue, Haiping Huang, Fu Xiao, Qi Li · 5 authors
Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.
The growth in the technology of cloud storage has meant that more and more organizations outsource large amounts of data to the cloud for storage, for archival and compliance purposes. This offers cost and scalability benefits but it introduces issues around verifying data integrity and the risk of leaking sensitive during audits by third parties. Past public auditing frameworks rely on interactive challenge-response mechanisms that leak metadata, increase audit latency and are susceptible to man in the middle attacks. This research proposes a non-interactive, privacy preserving public auding framework using Zero-knowledge Scalable Transparent Arguments of Knowledge (zk-STARKS). Using the Winterfell library to generate cryptographic proofs that a data set remains unchanged without revealing the content of the data or structural metadata to the verifier. The frame is deployed using a cloud native architecture, composed of AWS services. Evaluation of a 200MB dataset split into 2mb per block achieved proof and verification times of approx. 900 ms per block, which can be scaled up to larger datasets with limited cloud compute costs. The result confirm that zk-STARKS can be used to implement an efficient, trust less and privacy preserving framework for cold data auditing, detecting tampering of data without the need for a trusted TPA.
Abstract The aim of this study is to design and implement a system that allows centralized blockchain institutions to prove their solvency. This system ensures that institutions do not misappropriate user assets and enhances trust between users and institutions. The article introduces the Groth‐16 zero‐knowledge proof algorithm from ZK‐SNARK (zero‐knowledge succinct non‐interactive argument of knowledge). The R1CS arithmetic circuit in the Groth‐16 algorithm effectively guarantees the authenticity and tamper‐resistance of the system's raw data sources. Additionally, it combines the use of Merkle Sum Trees and Sparse Merkle trees. The former enables users to perform distributed verification of solvency proofs, while the latter effectively hides the overall number of users. Finally, users verify the balances and the private key signatures of addresses in the institution's bulletin board. Together, these components form a comprehensive and distributed solvency proof solution. This solution is a pioneering solution in the field of blockchain solvency proofs and provides a secure, efficient, and privacy‐preserving method for centralized cryptocurrency service providers or Web3 enterprise custodians. It effectively addresses the challenge of proving an institution's possession of sufficient reserves to cover user assets without compromising user privacy or disclosing the institution's scale.