Recently, increasing personal data has been stored in blockchain databases, ensuring data integrity by consensus. Although transparent and immutable blockchains are mainly adopted, the need to deploy preferences on which users canreadandeditthe data is growing in importance. Based on chameleon hashes, recent blockchains support editability governance but can hardly prevent data breaches because the data is readable to all participants in plaintexts. This motivates us to propose NANO, the first permissioned blockchain database that provides downward compatible readability and editability governance (i.e., users who caneditthe data can alsoreadthe data). Two challenges are protecting policy privacy and efficiently revoking malicious users (e.g., users who abuse their editability privileges). The punchline is leveraging Newton's interpolation formula-based secret sharing to hide policies into polynomial parameters and govern the distribution of data decryption keys and chameleon hash trapdoors. Inspired by proxy re-encryption, NANO integrates unique user symbols into user keys, achieving linear user revocation overhead. Security analysis proves that NANO provides comprehensive privacy preservation under the chosen-ciphertext attack. Experiments on the FISCO blockchain platform demonstrate that compared with state-of-the-art related solutions, NANO achieves a 7× improvement on average regarding computational costs, gas consumption, and communication overhead.
Abstract In this paper we introduce a scalable, privacy-preserving, federated learning framework, coined FLoBC, based on the concept of distributed ledgers underlying blockchains. This is motivated by the rapid growth of data worldwide, especially decentralized data which calls for scalable, decenteralized machine learning models which is capable of preserving the privacy of the data of the participating users. Towards this objective, we first motivate and define the problem scope. We then introduce the proposed FLoBC system architecture hinging on a number of key pillars, namely parallelism, decentralization and node update synchronization. In particular, we examine a number of known node update synchronization policies and examine their performance merits and design trade-offs. Finally, we compare the proposed federated learning system to a centralized learning system baseline to demonstrate its performance merits. Our main finding in this paper is that our proposed decentralized learning framework was able to achieve comparable performance to a classic centralized learning system, while distributing the model training process across multiple nodes without sharing their actual data. This provides a scalable, privacy-preserving solution for training a variety of large machine learning models. Graphical abstract
Abdullah J. Abualhamayl, Mohanad A. Almalki, Firas Al-Doghman, Abdulmajeed A. Alyoubi · 5 authors
Owning a house or investing in real estate can present challenges for many individuals. While fractional ownership offers potential solutions, establishing joint ownership among multiple participants poses obstacles that hinder its widespread adoption and limit accessibility. In response to these challenges, we propose JOINFT, a blockchain-based solution that utilizes fractional non-fungible tokens (F- NFTs) for facilitating joint ownership in real estate. JOINFT aims to enhance accessibility, empower individuals to participate in property ownership, and promote affordability while ensuring secure and transparent transactions. This paper provides an overview of the JOINFT system, including key participants, platform structure, functionality, and the utilization of the group level agreement. Additionally, our proposed method incorporates provenance information to capture user participation history within groups which contributes to the calculation of their trust score. To evaluate the effectiveness of JOINFT in enabling joint ownership through F-NFTs, we developed a prototype and conducted test transactions within an integrated development environment that supports decentralized applications and smart contracts. The evaluation demonstrates the effectiveness of utilizing F- NFTs for joint ownership in the real estate industry. It validates JOINFT’s ability to enhance accessibility, affordability, and transparency and trust in real estate transactions. Nevertheless, as JOINFT is an ongoing project, further research is required on regulatory frameworks, wider acceptance, and the potential of F-NFTs, trust scores, and provenance information in joint ownership in the real estate industry.
Rui Hu, Xinyi Liang, Sen Wang, Yu Liu · 5 authors
With the development of the Blockchain 3.0 era, there is an increased focus on combining smart contracts with other technologies. In the pension data scenario, traditional access control methods are no longer applicable due to complex data objects, numerous data operations, high granularity requirements and the need to support scalability. To address these issues, a more secure and flexible access control scheme is required. Therefore, this research abstractly describes the specific scenarios under pension data applications, combines smart contracts with an attribute-based access control model, designs and implements three smart contracts based on the Hyperledger Fabric blockchain architecture: Drive Contract, Access Contract, and Policy Contract. The solution has completed performance testing and can remain stable in the face of a large number of concurrent requests.
Shulei Zeng, Bin Cao, Yao Sun, Chen Sun · 6 authors
In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.
Access control is critical for collaboration and resource sharing between nodes in the Industrial IoT (IIoT). Existing blockchain combined with access control frameworks cannot address privacy protection issues and dynamic authorization of distributed IIoT equipment. Policy-domain-based access control (PDAC) is an access control scheme with dynamic authorization, granularity, and distributed decision making in a distributed environment. It abstracts unidentified nodes in a distributed system as policy domains and uses the policy domain’s certificate authority to issue access rights. This article proposes a smart-contract-based PDAC (SC-PDAC) framework to address the above problems in distributed IIoT. The framework mainly uses smart contracts to write SigContracts, RequestContracts, DecisionContracts, and DelegationContracts with different levels of security access control for different types of resources. To demonstrate the feasibility of the framework, a local private blockchain was created using the Ethereum blockchain system to conduct scenario experiments. The threat model and features of the framework were also analyzed, concluding that the framework has new features of agentability and controllability. In addition, the costs of existing access control frameworks were compared. The results show that the framework proposed in this article has relatively low computational and gas costs and is more suitable for distributed IIoT.
Digital Identity Management system is important component of security infrastructure for internet applications. However, existing digital identity management systems encounter various challenges, including difficulties in cross-domain authentication and interoperation, lack of credibility in identity authentication, and vulnerabilities in the security of identity data. Despite the attention blockchain technology has garnered in the field of digital identity management and the development of blockchain-based systems, these systems have not fully resolved the aforementioned problems. To address these issues and establish a secure and trustworthy digital identity management system, this paper proposes an effective model that integrates self-sovereign identity, oracle technology, and blockchain. This model aims to provide solutions and lay the groundwork for overcoming the challenges and ensuring the construction of a secure and reliable digital identity management system.
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani · 7 authors
For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.
Gaining significant attention within decentralized contexts, Federated Learning (FL) has been positioned as a highly desirable method for machine learning. By enabling multiple entities to train a shared model cooperatively, data privacy and security are preserved by Federated Learning. Harnessing inherent transparency and accountability of blockchain technology to trace and authenticate updates effectively in federated learning has transpired as an up-and-coming avenue to tackle data challenges related to confidentiality, protection, and reliability. This study examines the viability of federated learning and blockchain integration across multiple dimensions. The technological components of this integration., including incentive systems, consensus mechanisms, data validation, and smart contracts, are delved into. In the study, a novel proposed model for federated learning integrated with blockchain is designed and implemented. It is observed that the mean cypher size is 100 bytes for varying values of gradients. The average throughput recorded is 1.7 bytes per second, while the mean accuracy is 87.1% for 50 epochs.
Data privacy entails safeguarding users' control over information access, while data accessibility aims to ensure unrestricted availability of information. Inevitably, conflicts between privacy and accessibility arise, particularly within the healthcare sector. To tackle this challenge, we propose in this paper leveraging blockchain technology. By utilizing a peer-to-peer distributed network with a secure and shared ledger, blockchain provides an excellent solution for tracking and tracing records effectively.
Blockchain has been envisioned as an anonymous cryptocurrency framework and can be applied in various applications such as e-payment, share economics, and distributed ledger. Although an account is anonymous, privacy in terms of consumption behaviors still imposes leakage risks. For example, an adversary may infer the consumption capability related to an account further by analyzing the history of consumption records. It is thus of critical importance to design a solution to preserve the anonymity of both transaction amount and transaction peer’s identity, which is changeable because the amount must be still authenticated in anonymity. In this paper, we propose a scheme by using Pedersen commitment to anonymize the transaction amount, and together using ring signature to conceal the transaction peer’s identity while maintaining authentication. Especially, we further improve the security of anonymous authentication enabled by ring signature by introducing accountability, which can defend against double-spending attacks by penalization and empower auditability. The extensive performance and security analysis justify the applicability of the proposed scheme.
S. Priya, P. Sheela Rani, S. P. Chokkalingam, A Prathik · 8 authors
Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are sca ttered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy.
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.
K. Sudharson, S Rajalalakshmi, Mohan Raj K R, Dhakshunhaa moorthiy
With the increasing use of Internet of Things (IoT) devices, ensuring their security and privacy has become crucial. Due to its decentralized and immutable nature, blockchain technology has emerged as a potential solution for effective device management. This study proposes a trust-based framework for managing IoT devices using blockchain techniques. The framework utilizes a blockchain-based decentralized trust model and employs a consensus mechanism to ensure system integrity and security. The feasibility and effectiveness of the proposed approach are demonstrated through simulation experiments. The framework achieves a trust score accuracy of over 90%, 30% higher than the best-performing approach in previous studies. The consensus mechanism implemented in the framework also reduces the probability of a security breach by 50% compared to the most secure system in prior research. This study shows that the proposed trust-based framework is a promising solution for managing IoT devices using blockchain technology, offering significant improvements over existing approaches.
Sushruta Mishra, Soham Chakraborty, Kshira Sagar Sahoo, Muhammad Bilal
The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber-Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.
Venkatesan Muthukumar, R. Sivakami, Vinoth Kumar Venkatesan, J. Balajee · 7 authors
The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device’s information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (>70% SDI, >97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.
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.
Engin Zeydan, Luis Blanco, Josep Mangues, Şuayb S. Arslan · 5 authors
Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.
In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management.
Blockchain offers a cutting-edge solution for storing medical data, carrying out medical transactions, and establishing trust for medical data integration and exchange in a decentralized open healthcare network setting. While blockchain in healthcare has garnered considerable attention, privacy and security concerns remain at the center of the debate when adopting blockchain for information exchange in healthcare. This paper presents research on the subject of blockchain’s privacy and security in healthcare from 2017 to 2022. In light of the existing literature, this critical evaluation assesses the current state of affairs, with a particular emphasis on papers that deal with practical applications and difficulties. By providing a critical evaluation, this review provides insight into prospective future study directions and advances.