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5,430 results · page 61 of 227
Seyi Oladimeji, Peter Broklyn, Axel Egon
No abstract is available for this record.
Harshini Kannoori, Pooja Hanavadi Balaraju, Ch Lakshmi Harika, Susmita Mandal
No abstract is available for this record.
Nan Wang, Yuqin Luo, Hao Liu, Haibo Tian
Financial institutions using blockchain smart contracts need to adhere to real-world regulations. Data on blockchain is easily accessible, so privacy protection is crucial. Our goal is to introduce an efficient protocol that satisfies both user privacy protection and hierarchical regulatory requirements, without the need for zero-knowledge proofs. To achieve this, we have developed two innovative design strategies. Firstly, we envision financial institutions serving as transaction mixers for their users. This approach offers an additional layer of privacy by obfuscating the source of each transaction. Secondly, we depend on regulatory agencies to oversee the compliance of blockchain transactions. This ensures that our protocol aligns with regulatory requirements while maintaining user privacy. The resulting protocol offers superior privacy protection for user transactions, with provable security and computational efficiency.
Yang-tao Wang, Xin Liu, Shuo Liu, Shuo Liu · 10 authors
No abstract is available for this record.
Asha Sanap, Sulakshana Malwade, Rohini Bhosale, Aarti Karandikar · 6 authors
Blockchain-based solutions offer a promising avenue for privacy-preserving authentication and authorization mechanisms. Through the immutable and decentralized nature of blockchain, individuals can maintain control over their personal data while still engaging in secure transactions and interactions. These solutions leverage cryptographic techniques to ensure privacy, such as zero-knowledge proofs, which allow one party to prove possession of certain information without revealing the information itself.By storing authentication and authorization data on the blockchain, users can access services without having to repeatedly provide sensitive information. Smart contracts can automate authorization processes, ensuring that only authorized parties can access certain resources or perform specific actions. Additionally, blockchain-based identity systems offer a self-sovereign approach, where individuals have full control over their digital identities, reducing the reliance on centralized authorities. Moreover, blockchain networks provide transparency and auditability, allowing users to track how their data is being used and ensuring compliance with privacy regulations. However, challenges such as scalability, interoperability, and user adoption remain to be addressed for widespread implementation. Overall, blockchain-based solutions hold great potential in providing privacy-preserving authentication and authorization while empowering individuals with greater control over their data.Blockchain-based solutions offer a promising avenue for privacy-preserving authentication and authorization mechanisms. Through the immutable and decentralized nature of blockchain, individuals can maintain control over their personal data while still engaging in secure transactions and interactions. These solutions leverage cryptographic techniques to ensure privacy, such as zero-knowledge proofs, which allow one party to prove possession of certain information without revealing the information itself.
Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi
No abstract is available for this record.
Fatemah H. Alghamedy, Nahla El-Haggar, Albandari Alsumayt, Zeyad M. Alfawaer · 8 authors
The rapid advancement of technology has set higher standards for the next generation of wireless communication networks, known as 6G. These networks go beyond the simple task of connecting devices and aim to establish a self-sustaining system within society. One of the key factors in achieving this goal is the integration of AI services and apps through the Internet of Things (IoT), which will be made possible with the support of 6G technology. The advancement of artificial intelligence (AI) will play a crucial role in enhancing the protocols, architectures, and operations of 6G networks. To achieve collaborative AI in IoT applications, Federated Learning (FL) has emerged as a popular method. FL enables AI training without the need for data sharing, ensuring privacy and security. However, FL also faces challenges, such as the presence of malicious data and the risk of single-point failure. To address these concerns, blockchain technology (BCT) offers a secure and efficient solution. By leveraging blockchain, these issues can be effectively tackled, providing a reliable framework for implementing FL-IoT applications.
Yuan Wang, Lin Sun
The secure sharing and privacy protection of medical data have become pain points for medical data management platforms. Therefore, a secure sharing electronic health record privacy protection method based on blockchain is proposed in the study, aiming to improve data security privacy and ensure absolute ownership of patients' medical data. Attribute encryption and blockchain computing are utilized to construct a data secure sharing model, and zero-knowledge proof and ElGamal encryption algorithms are introduced to further improve the construction of data privacy protection methods. Experimental verification showed that the data secure sharing method proposed in the study has more advantages in terms of production key size and time cost. Compared with other public recognition mechanisms, zero-knowledge proof reduced the average time cost of generating keys by 54.36%. The proposed data privacy protection method had an average increase of 7.73% in protection effectiveness compared to other methods. The results indicate that the data secure sharing and privacy protection methods proposed in the study can improve the overall performance and security of the system while fully ensuring the absolute ownership of patients' data. This method has positive application value in the privacy protection of medical data.
Tran Thi Phuong, Le Trieu Phong
No abstract is available for this record.
Yuming Tang, Yitian Zhang, Tao Niu, Zhen Li · 7 authors
Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.
Jisheng Dong, Qingni Shen, Junkai Liang, Cong Li · 6 authors
No abstract is available for this record.
Masayuki Abe, Miguel Ambrona, Miyako Ohkubo
No abstract is available for this record.
Prabhanjan Ananth, Gilad Asharov, Vipul Goyal, Hadar Kaner · 6 authors
No abstract is available for this record.
Zainab Khalid Mohammad, Salman Bin Yousif, Yunus Bin Yousif
Abstract The metaverse, a virtual multiuser environment, has garnered global attention for its potential to offer deeply immersive and participatory experiences. As this technology matures, it is evolving in tandem with emerging innovations such as Web 3.0, Blockchain, nonfungible tokens, and cryptocurrencies like Bitcoin, which play pivotal roles in the metaverse economy. Robust Bitcoin networks must be modelled for the metaverse environment in Industry 5.0 platforms to ensure the metaverse’s sustained growth and relevance. Industry 5.0 is poised to experience significant economic expansion, driven in large part by the transformative influence of metaverse technology. Researchers have actively explored diverse strategies and approaches to address the unique challenges and opportunities presented by current Bitcoin networks, highlighting the limitless potential for enhancing anonymity and privacy while navigating this exciting digital frontier. By addressing the diverse anonymity and privacy evaluation attributes, the lack of clarity regarding the prioritisation of these attributes and the variability in data, this modelling approach can be categorised as a form of multiple attribute decision-making (MADM). This review seeks to achieve three main objectives: firstly, to identify research gaps, obstacles, and problems within scholarly literature, which is crucial for assessing and modelling Bitcoin networks to succour the metaverse environment of Industry 5.0; secondly, to pinpoint theoretical gaps, proposed solutions, and benchmarking of Bitcoin networks; and thirdly, to offer an overview of the existing validation and evaluation methods employed in the literature. This review introduced a unique taxonomy by intersecting “Bitcoin networks based on blockchain aspects” with “anonymity and privacy development attributes aspect.” It emphasised the study’s significance and innovation. The results illustrate that employing MADM techniques is highly suitable for modelling Bitcoin networks to support the metaverse within the context of Industry 5.0. This thorough review is an invaluable resource for academics and decision-makers, offering perspectives regarding the improvements, applications, and potential directions for evaluating Bitcoin networks to bolster the metaverse environment of Industry 5.0.
Apurva K. Vangujar, Alia Umrani, Paolo Palmieri
No abstract is available for this record.
He Qin, Xiaofeng Ma, Dawei Zhang, Feng Peng
Decentralized identity represents an innovative approach based on blockchain to achieve effective identity management. This method utilizes decentralized identifiers and verifiable credentials to enable trusted authentication, free circulation of identity information, and self-sovereign control over identity data functionalities. The current decentralized identity systems rely on entirely anonymous identifiers, lacking robust identity regulation. Furthermore, they face challenges such as identity attribute leakage during verifiable credential presentation and the issuers’ struggle to reliably revoke credentials. To address these issues, efficient and practical schemes have been designed based on BBS signature, zero-knowledge proof, dynamic accumulator, and blockchain technology: one for decentralized identifiers management and the other for verifiable credential privacy protection, both of which are supervised and revocable. The former ensures the privacy of subject identity while achieving regulatability and revocability of identity data by the regulator. The latter facilitates selective disclosure of anonymous credentials and reliable revocation. A security analysis shows that the proposed scheme meets anonymity, non-forgeability, regulatory reliability, and revocability reliability, and offers comprehensive and effective privacy protection measures. The experimental results demonstrate that the algorithms designed operate at a millisecond level, which satisfies the demands of blockchain identity management scenarios.
Rohit Reddy Chananagari Prabhakar
The application of Artificial Intelligence (AI) in educational analytics has ushered in unprecedented enhancement in student learning prediction, learning at scale, auto-grading, and institution-level decision-making. However, the increased generation and processing of student information precipitate unprecedented concerns in privacy and security, spanning breaches and inference attacks through adversarial manipulations, unauthorized third-party information extraction, and AI model explainability restrictions. In this article, we provide a critical overview of privacy-preserving AI-based educational analytics databases, from state-of-the-art approaches such as Differential Privacy (DP), Federated Learning (FL), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain. Global regulation compliance regimes such as the General Data Protection Regulation (GDPR), the Family Educational Rights and Privacy Act (FERPA), and the California Consumer Privacy Act (CCPA) are reviewed, with the ethical trade-offs and conflicts between utility and privacy preservation laid bare. Projected future directions from Zero-Knowledge Proofs (ZKP) and decentralized AI platforms through hybrid AI-privacy architecture and explainable AI (XAI) are discussed.
Romain Gay, Bogdan Ursu
No abstract is available for this record.
Cheng Huang, Xuemin Shen
No abstract is available for this record.
Zeng Huang, Ming‐Tian Zhang, Tengfei Liu, Anjia Yang
Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus not made the selection honestly. This lack of trustworthiness problem, which can demotivate participants, has received little attention. Another problem that has received little attention is the leakage of participants’ private information during the selection process. We will therefore propose a federated learning framework with auditable participant selection. It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy. Considering the possibility of malicious campaigning and impersonation of participants, the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors. Finally, we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.
Saptarshi De Chaudhury, Likhith Reddy Morreddigari, Matta Varun, Tirthankar Sengupta · 8 authors
Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP). The nodes that share less obfuscated data under LDP are awarded higher quantum of tokens, which they can later use to obtain session keys for accessing encrypted model parameters updated by the server. If one or more of the nodes do not contribute to the learning process by sharing their data, or share only highly perturbed data, they earn less number of tokens. As a result, such nodes may not be able to read the new global model parameters if required. Local parameter sharing and updating of global parameters are done using the distributed ledger of a permissioned blockchain, namely HyperLedger Fabric (HLF). Being a blockchain-based approach, the risk of a single point of failure is also mitigated. Appropriate chaincodes, which are smart contracts in the HLF framework, have been developed for implementing the proposed methodology. Results of an extensive set of experiments firmly establish the feasibility of our approach.
D. Saveetha, G. Maragatham, Vijayakumar Ponnusamy, Nemanja Zdravković
Blockchain networks serve as a transparent and secure ledger storage solution, yet they remain vulnerable to attacks. There must be some mechanism to protect the blockchain network from attacks. Among various attacks, the Distributed Denial of Service (DDoS) attack is considered severe, which is challenging to detect accurately and reliably. Machine learning techniques are used to detect the attack, which requires exploring all global attack data in a single system, which is difficult in practice. This article proposes a distributed machine learning mechanism called Federated Machine Learning for detecting the presence of DDoS attacks. But in federated machine learning the model itself can be poisoned by the malicious collaborating node which is another problem that this article solves by storing the model in blockchain and by introducing a new reputation-based miner selection procedure. The proposed framework integrates the federation of machine learning within the blockchain network framework for detecting DDoS attacks. Under the integrated framework, miners are used to train the blocks and they also participate in the machine learning training. A dynamic reputation-based miner selection mechanism that can balance exploration and exploitation is proposed for optimal miner selection, which can ensure the high accuracy of the machine learning model and improve the security of blockchain from attacks like DDoS attacks and 51% attacks. The proposed framework is tested with Random Forest, Multilayer Perceptron, and Logistic Regression machine learning algorithms. The proposed mechanism achieved maximum accuracy of 99.1% using random forest model which is superior to the existing mechanism of detection of DDoS attacks.
Weiqi Dai, Jinkai Liu, Yang Zhou, Kim‐Kwang Raymond Choo · 7 authors
While blockchain is known to support open and transparent data exchange, partly due to its nontamperability property, it can also be (ab)used to facilitate the spreading of fake and misleading information or information that was subsequently discredited. Hence, this paper proposes a practical, redactable blockchain framework with a public trapdoor (hereafter referred to as PRBFPT). PRBFPT comprises an editing scheme for adding blocks using a new type of blockchain with a chameleon hash. Specifically, PRBFPT is able to involve all nodes in the blockchain in the editing operations by means of a public trapdoor, without requiring additional trapdoor management by predefined nodes or organizations. PRBFPT is also designed to audit and record the content of each editing operation. In other words, after editing and deleting the original data, PRBFPT can still verify its legitimacy. We also propose a contract-based locked voting scheme to better support voting. We then evaluate the prototype implementation of PRBFPT, whose findings show that the total time consumption of adding modules is at the millisecond level, with a negligible impact on the performance of the original system. In addition, the evaluation findings show that the cost of initiating the special transactions is comparable to the consumption of normal Ethereum transactions and is within a manageable range.
Irimia Cosmin-Iulian, Adrian Iftene
This paper introduces a microservice-based architecture to revolutionize how official documents are shared, verified, and stored in digital formats. Addressing the pressing issues of privacy, security, and trust, the proposed solution enables the partial and full sharing of documents while safeguarding sensitive data. The architecture comprises seven core microservices, including a data extractor, document obfuscator, notarization service, and decentralized storage through blockchain and IPFS. By obfuscating designated document fields and utilizing a distributed ledger for notarization, the system ensures both the privacy of users and the transparency required for official verification. Through the combination of state-of-the-art encryption techniques, video-based notarization, and blockchain for immutability, this approach enables secure, scalable, and privacy-conscious document sharing. Detailed guidelines are provided for each microservice, from data extraction and field obfuscation to notarization and decentralized storage. Our approach resolves common issues such as data tampering, unauthorized access, and identity fraud while offering a framework for future innovations in digital notarization.