Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter challenges like data silos, privacy breaches, and transmission latency. In response to these challenges, this paper introduces a blockchain-based framework for trustworthy medical data sharing in edge computing environments. Leveraging healthcare consortium edge blockchains, this framework enables fine-grained access control to medical data. Specifically, it addresses the real-time, multi-attribute authorization challenge in CP-ABE through a Distributed Attribute Authorization strategy (DAA) based on blockchain. Furthermore, it tackles the key security issues in CP-ABE through a Distributed Key Generation protocol (DKG) based on blockchain. To address computational resource constraints in CP-ABE, we enhance a Distributed Modular Exponentiation Outsourcing algorithm (DME) and elevate its verifiable probability to "1". Theoretical analysis establishes the IND-CPA security of this framework in the Random Oracle Model. Experimental results demonstrate the effectiveness of our solution for resource-constrained end-user devices in edge computing environments.
Abstract The application of healthcare systems has led to an explosive growth in personal electronic health records (EHRs). These EHRs are generated from different healthcare institutions and stored in cloud data centers, respectively. However, data owners lose the authority to control and track their private and sensitive EHRs. In fact, data owners cannot establish rules for EHRs exchanging and sharing, nor can they verify the integrity of EHRs stored in semi-trusted clouds. Hence, a individual-centric access control framework is required to realize data access control. In this study, we construct a data access control framework, which integrates the decentralized smart contracts and role-based access control (RBAC) to provide fine grained data access control services. Besides, this scheme allows data owners to define access control policies using JavaScript Object Notation (JSON) and further translate these policies into immutable blockchain transactions. Furthermore, we combine blockchain and bilinear mapping to design a data integrity verification system for users to check the integrity of their cloud stored EHRs. Finally, we analyze the security of this scheme and develop a prototype system to evaluate the performance. Both theoretical analysis and experiment results indicate that this scheme can realize fine-grained data access control and efficient data integrity verification
Digital identity has rapidly gained interest as organisations strive toward digital transformation. However, the lack of adequate digital identity management mechanisms has become a major concern. Consequently, decentralised digital identities are increasingly being used as a form of credentialing. This requires secure, reliable, scalable and privacy-preserving credential issuance and verification mechanisms to prove the legitimacy of the credential. On the other hand, most existing systems provide centralised solutions that primarily rely on third parties. Meanwhile, these systems do not allow end-users to own, control, and share their credentials with relevant parties. This paper proposes a secure and reliable de centralised digital identity, verifiable credential tracking, and management system. The proposed system guarantees the authenticity and integrity of the user credentials and allows users to selectively share a subset of their verifiable credentials with relevant stakeholders through the model known as self-sovereign identity (SSI). SSI facilitates end users to have sole ownership over the ability to control their accounts and personal data. To achieve the objectives of the study, the proposed system adopts the World Wide Web Consortium (W3C), Decentralised Identifiers (DIDs), Verifiable Credentials (VCs), and Distributed Ledger Technologies (DLTs). The system outperforms competitors in terms of both storage and speed and, thus, delivers exceptional efficiency and interoperability.
Wensheng Tian, Lianfu Zhang, Shuangxi Chen, Wang Hu · 5 authors
The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms, etc. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.
Nils Fleischhacker, Gottfried Herold, Mark Simkin, Zhenfei Zhang
Multi-signatures allow for compressing many signatures for the same message that were generated under independent keys into one small aggregated signature. This primitive is particularly useful for proof-of-stake blockchains, like Ethereum, where the same block is signed by many signers, who vouch for the block's validity. Being able to compress all signatures for the same block into a short string significantly reduces the on-chain storage costs, which is an important efficiency metric for blockchains.
Web3 applications based on blockchains regularly need access to randomness that is unbiased, unpredictable, and publicly verifiable. For Web3 gaming applications, this becomes a crucial selling point to attract more users by providing credibility to the "random reward" distribution feature. A verifiable random function (VRF) protocol satisfies these requirements naturally, and there is a tremendous rise in the use of VRF services. As most blockchains cannot maintain the secret keys required for VRFs, Web3 applications interact with external VRF services via a smart contract where a VRF output is exchanged for a fee. While this smart contract-based plain-text exchange offers the much-needed public verifiability immediately, it severely limits the way the requester can employ the VRF service: the requests cannot be made in advance, and the output cannot be reused. This introduces significant latency and monetary overhead.
Rofaida Khemaissia, Makhlouf Derdour, Mohamed Amine Ferrag, Mohammed Mounir Bouhamed
The main goal of a Data Service Composition is combining multiple data services to provide for a user’s query a new service which uses data from multiple service providers that are incorporated in the composition. In this situation, the data privacy and especially of the service providers can be breached when their critical data can be seen by another party. Therefore, keeping the data privacy during the composition process is crucial by every work in the context of the service composition. Recent approaches rely on a central mediator that can be trusted or not to ensuring the privacy of the service providers during the query execution. The most recent approaches found problems in case of untrusted mediator where they enforce restrictions like k-protection that can affect the efficiency of their works. Therefore, we propose PrSChain which preserves the privacy of all service providers during service composition and execution using BlockChain technology. We used a permissioned BlockChain that acts as trusted mediator where it enables users to access to the BC if a valid certificate is given. We use Hyperledger Fabric to implement our solution where it stores sensitive data about the composition plan. In addition, the intermediate query results are saved in IPFS that acts as offchain storage. As a proof of concept, we have tested PrSChain on a real-world medical dataset to show its feasibility and efficiency for maintaining privacy in a secure and trusted manner.
Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.
Rail transit is a commonly used mode of transportation for daily commuting, but traditional Rail Transit Systems (RTS) typically face significant challenges such as high mobility and low latency. Furthermore, the RTS generates a substantial volume of real-time interactive data, posing difficulties in ensuring user privacy while maintaining practicality. To address these concerns, blockchain as a promising technology, has been leveraged to safeguard data transmission and access. However, public blockchains such as Bitcoin and Ethereum may not be suitable for real-time interactive RTS scenarios due to their lack of flexibility and control. To overcome this, the notion of the Policy-Driven Blockchain (PDB) was introduced, which is a private blockchain with flexible policy control and management mechanisms. PDB can be quickly customized and deployed according to specific business requirements and rules, making it a valuable feature for RTS scenarios. However, their scheme relies on bilinear pairings, which incurs high computational overhead and does not support batch validation. In this article, we propose a Lightweight Policy-Driven Blockchain (LPDB) scheme to overcome these challenges, the core of which is the design of a Pairing-free Policy-Driven Signature (PFPDS) scheme better tailored to meet the specific requirements in RTS. In addition, considering the large number of communication messages in RTS, we analyze the advantages of batch verification of signatures. In conclusion, the security of our proposal is validated under the random oracle model, and we evaluate the computational expenses associated with the algorithms via simulations on a laptop. The implementation outcomes demonstrate the practicality and viability of our scheme.
This study paper explores the critical topic of formal verification of smart contracts in distributed ledger technology (DLT) systems. Smart contracts, self-executing code running on blockchain platforms, have gained widespread adoption in various industries due to their automation and transparency benefits. However, the decentralized and immutable nature of DLT raises concerns regarding smart contract security, leading to vulnerabilities and potential exploits. Formal verification methods offer a systematic approach to analyze smart contracts for correctness and safety. This paper provides an overview of smart contracts, DLT, and the importance of formal verification. It reviews various formal verification techniques and presents state-of-the-art tools and frameworks. The paper concludes with discussions on challenges, future research opportunities, and the role of formal verification in enhancing DLT-based applications' security and usability.
Blockchain-based data storage has become an emerging paradigm, providing a fair and transparent data platform for decentralized applications. However, how to achieve secure on-chain verification for arbitrary SQL queries in such a decentralized storage remains under-explored. Due to the limitations of authenticated data structure (ADS), existing works either do not consider arbitrary query verification issue or fail to achieve practical gas consumption efficiency. In this paper, we present a novel arbitrary query verification scheme for decentralized storage. The proposed scheme, named$\mathsf {zkQuery}$, enables efficient public verification for arbitrary queries with zero-knowledge confidentiality.$\mathsf {zkQuery}$is built from the ingenious synergy of techniques from both zero-knowledge proof and smart contract technology. The core idea is to delegate smart contracts to fairly execute results verification and utilize our tailored zero-knowledge proof protocol to facilitate arbitrary computation in a privacy-preserving manner. The verification protocols of$\mathsf {zkQuery}$are highly customized for decentralized storage, where the complexity of on-chain verification can be completed in logarithmic time, significantly decreasing gas consumption. We rigorously provide security analysis and complete the prototype implementation. The extensive experiments over the NEAR blockchain show that$\mathsf {zkQuery}$can gain at least$2\times $better performance than the baseline approach on all metrics.
Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing${n}$shares in the way that a set of any${k}$shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal’s encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal’s private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal’s public key from a deployed smart contract on blockchain and recover ElGamal’s private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization.
Web3 systems expose a fundamentally different security landscape from centralized platforms, characterized by composability, pseudonymous identities, decentralized governance, and rapidly evolving attack strategies that span social, application, and protocol layers. Existing security mechanisms, such as static smart contract analysis, blacklist-based phishing detection, and network-level mitigation, operate in isolation and assume fixed threat models, limiting their effectiveness against adaptive, cross-layer adversaries. This position paper argues that securing Web3 requires a shift from static, tool-centric defenses to learning-driven security primitives capable of continuous reasoning, adaptation, and actuation. We introduce AI-powered smart certificates as a new security abstraction: programmable, continuously updated trust artifacts that integrate on-chain verifiability with off-chain machine learning signals derived from user behavior, transaction dynamics, and social context. Unlike traditional certificates or audits, these certificates maintain state, learn under distribution shift, and support automated policy enforcement and revocation in response to evolving threats. We argue that existing paradigms, formal verification, threat modeling, and isolated anomaly detection, are structurally limited in capturing the non-stationary and socio-technical nature of Web3 attacks. We outline an architecture in which AI-powered smart certificates serve as cross-layer sentinels that coordinate heterogeneous security signals in real time, and position smart certificates as a research direction, raising questions around learning under partial observability, adversarial adaptation, and trustworthy ML deployment in decentralized systems.
Abstract Blockchain technology has gained widespread adoption in recent years due to its ability to enable secure and transparent record-keeping and data transfer. A critical aspect of blockchain technology is the use of consensus algorithms, which allow distributed nodes in the network to agree on the state of the blockchain. In this review paper, we examine various consensus algorithms that are used in blockchain systems, including proof-of-work, proof-of-stake, and hybrid approaches. We go over the trade-offs and factors to think about when choosing a consensus algorithm, such as energy efficiency, decentralization, and security. We also look at the strengths and weaknesses of each algorithm as well as their potential impact on the scalability and adoption of blockchain technology.
Michael Eckel, A. Basu, Satoshi Kai, Hervais Simo Fhom · 8 authors
In this paper, we present a framework and an architecture that aim to enable and manage trust in supply chains. Our architecture addresses the authenticity and integrity of devices and processes within heterogeneous system landscapes. We identify and discuss the current challenges in digital supply chains and lay out security, privacy, and interoperability requirements that must be met for successful implementation. We hypothesize that the overall perception of trust in a supply chain depends on the trustworthiness of all digital systems involved, including hardware, software, and information flow. Our proposed architecture helps enhance trustworthiness based on verifiable, indisputable, and believable digital evidence for devices and processes in supply chains, including the entire hardware and software lifecycles. We actively advocate for a mixed landscape of centralized and decentralized solutions for the storage of evidence and trust information. This can include traditional centralized databases and distributed ledger technologies. We discuss the auditability and accountability of digital evidence using trust-enabling technologies, and present a preliminary proof-of-concept (PoC) implementation in a real-world application scenario.
Physical Unclonable Functions (PUFs) and Hardware Security
The Internet of Things (IoT) has witnessed tremendous growth in recent years, connecting billions of devices and enabling the exchange of vast amounts of data. Trust management plays a crucial role in establishing reliable and secure interactions among IoT devices and entities. This paper proposes a Smart Contract for Trust Management (SCoTMan) model that employs social relationships and adaptive weight adjustment to achieve efficient trust management in the IoT, leveraging the benefits of blockchain technology. Blockchain facilitates trust management by providing a global perspective on trust values among devices and enhancing trust propagation and updates. The model has been implemented and tested on the Hyperledger Fabric platform, utilizing IoT sample devices. Various experiments have been conducted to evaluate and validate the efficiency of the model. The model demonstrates improved performance in terms of trust convergence, and global storage cost, which is defined in this paper.
Nowadays, data are regarded as an intangible asset. In the data marketplace, data are treated as a commodity or service, data owners can gain economic revenue by selling their data ownership or data usage rights. However, the data marketplace is facing various threats and challenges, such as unauthorized data reselling, trade of bogus data, dishonest data ownership claims, and unreasonable revenue allocation. Particularly, in the process of data resale, the revenue allocation remains a challenge when the data are processed and resold in another format. To solve this problem, we propose a revenue allocation mechanism based on the referable Non-Fungible Token (rNFT) and Shapley value method. Firstly, we tokenize the data to NFT to ensure the data ownership is traceable; Secondly, we use rNFT to record the data lineage that ensures the data owner can participate in the revenue allocation when the data are resold; Finally, we calculate the contribution of each party by the Shapley value method to ensure fairness in revenue allocation. We implement a prototype of our scheme on Ethereum and evaluate it comprehensively. The test results indicate that our scheme can meet the performance requirements of the data marketplace and improve the revenue of data owners effectively.
In an era characterized by the rapid expansion of cloud storage services and the ubiquitous digitization of data, preserving the security and privacy of information has become paramount. This research paper explores a pioneering approach to fortify data security in cloud storage through the application of zero-knowledge proof protocols.The growing reliance on cloud storage has exposed organizations and individuals to various security threats, including data breaches, unauthorized access, and privacy concerns. Zero-knowledge proof protocols, such as zk-SNARKs and zk-STARKs, offer a unique solution to address these challenges. They allow for the verification of data integrity and authenticity without exposing the actual data, thereby ensuring data privacy in the cloud.This paper comprehensively delves into the principles and mechanisms of zero-knowledge proofs and their practical application in cloud security. It discusses the benefits of using these protocols, including enhanced data security, reduced risk of data breaches, and compliance with data protection regulations.The research also considers the challenges and limitations associated with the integration of zero-knowledge proof protocols in existing cloud infrastructures, addressing computational overhead and user adoption hurdles. Case studies and practical implementations from various organizations highlight the real-world impact and benefits of these protocols.Ethical and regulatory considerations surrounding enhanced data security and privacy are examined, emphasizing the alignment of zero-knowledge proof protocols with data protection regulations and compliance requirements.As data security continues to be a critical concern for individuals and organizations in the digital age, the findings of this research provide valuable insights into an evolving landscape where technology and privacy intersect. It encourages further research, exploration, and adoption of zero-knowledge proof protocols to ensure the integrity and confidentiality of data in cloud storage, safeguarding the digital assets of users and enterprises alike.
G. Sucharitha, G Aditya, Julakanti Varsha, Gadilli Sai Nikhil
Blockchain technology has emerged as a crucial tool for ensuring security and reliability in various domains, particularly in healthcare. In this study, we utilize blockchain to establish an append-only chain of transaction blocks, ensuring the integrity and security of patient medical records. By employing blockchain, we aim to safeguard patient data, grant specific clinicians’ access to medical records, and ensure user privacy. The doctor will only receive prescription information after the patient has granted access, ensuring comprehensive protection for both parties. Consensus mechanisms within the blockchain guarantee consistency among blocks and require agreement from existing nodes before adding new transactions. Traditional healthcare systems often result in delays in data exchange and strict restrictions on access due to concerns about sensitive data leakage. By integrating blockchain technology into healthcare records and data, this article seeks to enhance data sharing while mitigating the risks of data tampering and security breaches.
The rapid growth and integration of the Internet of Things (IoT) emphasizes the crucial need for effective data governance. This research unveils a novel framework, capitalizing on blockchain and smart contracts, aimed at decentralizing data governance in the IoT sphere. Our approach allows stakeholders to formulate and enforce data governance collaboratively, ensuring a balance between transparency, adaptability, and flexibility. Using the Ethereum platform and Solidity as our smart contract language, we constructed a demonstrative proof-of-concept. Our comparative evaluations highlighted our system's superiority, outpacing previous works with a scalability score of 95%, flexibility at 90%, and an unmatched transparency score of 100%. This framework presents a transformative paradigm for organizations and individuals working with IoT data, offering an efficient, transparent, and robust data governance mechanism.