Follow blockchain research across journals, conferences, and preprint repositories.
3,460 results · page 22 of 145
Prity Rani, Rohit Kumar Sachan, Sonal Kukreja
No abstract is available for this record.
Zhongkai Lu, Lingling Wang, Zhengyin Zhang, Mei Huang · 6 authors
Federated learning is a widely used method for collaborative machine learning without sharing local data. In this approach, participants train models using their local data, and the model updates are aggregated into a global model. However, ensuring trustworthy model training is crucial because malicious participants may not use their actual local data or may not train the model as intended, which makes it challenging to guarantee the authenticity of the data and the integrity of the model training. To address these issues, we propose a trustworthy model training scheme (TMT-FL) with verifiable authenticity and integrity. Specifically, we leverage zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) based proofs to verify the integrity of the training execution. To deal with the performance bottleneck in generating zk-SNARK proofs, we use the Chinese Remainder Theorem to optimize the convolution operation, and present an improved zk-SNARK based proof generating scheme which significantly reduces the online proving time. Besides, we adopt matrix commitment along with bloom filter to ensure the authenticity and integrity of the training datasets. Extensive experimental results demonstrate that our improved zk-SNARK scheme performs nearly$3.1\times$faster than the state-of-the-art in online proving time. Moreover, we experimentally confirm the efficiency of TMT-FL under diverse datasets in terms of computational costs, storage costs, and communication overheads.
Yuxiao Song, Daojing He, Minghui Dai, Sammy Chan · 6 authors
Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security.
Somnath Agatrao Zambre, Namdev M. Sawant
No abstract is available for this record.
Wei Wang, Haipeng Peng, Junke Duan, Licheng Wang · 6 authors
The immutability of blockchain has exposed its limitations in adapting to rapidly evolving legal requirements and preventing malicious misuse. To address these issues, transaction-level redactable blockchain solutions based on the policy-based chameleon hash (PCH) have been introduced. These solutions allow users to create transactions and encrypt trapdoors under specific attribute policies. However, current transaction-level rewriting schemes face two security challenges: Firstly, transactions encrypted with the invalid trapdoor are difficult to rewrite; Secondly, due to lacking version detection on transactions, malicious modifiers may rollback the version of the transaction to launch a reversion attack. In this paper, we present a resilient and redactable blockchain (RRB) with 2-level rewriting and transaction version detection. Specifically, we propose a new redactable blockchain structure that supports both transaction-level and block-level rewriting. To tackle the invalid trapdoor problem, we propose two protocols: a fine-grained, controllable transaction-level rewriting protocol and a centrally controlled block-level rewriting protocol. Moreover, for the transaction reversion attack, we design a version detection mechanism for RRB by using an accumulator. Through security analysis and performance evaluation, we demonstrate the security and practicality of our RRB scheme.
Medhavi Malik, Nancy Arya, Nagamuthu Krishnan S S, Amrita Parashar · 6 authors
The purpose of this research paper is to look at the usage of Blockchain technology to improve the security of Cloud Computing Service Providers (CCSPs). The key goals of this research are to investigate the possible benefits and drawbacks of combining Blockchain with cloud computing, to perform a thorough review of relevant literature in the field, and to understand the fundamental mechanisms involved in this system. This research paper highlights significant benefits such as better data integrity, increased openness, and decentralized trust mechanisms through a comprehensive evaluation of previous studies. However, it is critical to recognize constraints such as scaling issues and significant performance overhead. The suggested solution comprises utilizing Blockchain's immutable and distributed ledger to securely store and verify key data such as user credentials, access rights, and service level agreements. This study evaluates the advantages and disadvantages of blockchain technology in enhancing cloud security and fostering trust between CSPs and their customers. It does so by drawing on existing literature and related research articles. CCSPs can create a more robust and secure environment by decentralizing trust and implementing cryptographic procedures. This study adds to our understanding of Blockchain technology's potential in cloud computing security and gives ideas for future research and practical deployment in real-world settings. In order to solve security problems, this research paper emphasizes the importance of incorporating blockchain technology into cloud computing service providers. In order to address the issues related to scalability, regulation, and interoperability, it emphasizes the necessity for additional research and development. The conclusions made in this study add to the body of knowledge on blockchain-based security solutions for cloud computing and offer important information to researchers, practitioners, and legislators who are working to improve the security of cloud computing service providers.
Prasant Kumar Dash, Shreya Suman, Rishav Kumar, Divya
The rapid growth of the Internet of Things has ledto an unprecedented increase in the volume of data generatedand shared across various devices and platforms. Consequently, the surge in data generation and sharing has heightened theneed for robust security measures, as these interconnected IoT devices are often susceptible to a multitude of cyber threats, ranging from unauthorized access and data breaches to malicioustampering and disruption of critical services. This paper presentsIoTChain, an advanced decentralized framework for secure storage and data sharing in Internet of Things (IoT) ecosys-tems. The proposed model ensures end-to-end encryption and precise, fine-grained access control through a role-based access control (R-BAC) system. IoTChain incorporates the Ethereum blockchain as a trusted, decentralized access management layer, leveraging smart contracts to guarantee secure and transparent access rights. By integrating Ethereum with the InterPlanetary File System (IPFS), the model provides decentralized, scalable, and fault-tolerant storage. Security is reinforced using Advanced Encryption Standard (AES) for data encryption, while secure keyexchange is achieved through the elliptic curve Diffie-Hellman (ECDH) protocol. To optimize efficiency, the traditional proof- of-work (PoW) consensus mechanism is replaced with proof- of-authority (PoA), significantly reducing transaction costs and boosting throughput.
D. W. K. Man, Haoyu Tai
This study focuses on data encryption and privacy protection technologies in cloud computing environments. By systematically implementing and evaluating various encryption algorithms (such as AES, RSA, and homomorphic encryption) and privacy protection techniques (including data masking, differential privacy, secure multi-party computation, and zero-knowledge proofs), the feasibility and effectiveness of these technologies in cloud environments are explored. A simulated cloud environment was constructed for experiments, and the results indicate that AES performs excellently in large-scale data processing, while homomorphic encryption demonstrates unique advantages in specific scenarios. Privacy protection techniques can achieve a balance between protecting user privacy and maintaining data availability. System performance and security tests confirm that the proposed solutions effectively support the data security and privacy protection needs in large-scale cloud environments. This research provides a comprehensive technical implementation and evaluation reference for data security and privacy protection in cloud computing environments, while also highlighting some challenges and offering valuable insights for future research directions.
Wenrui Zhao
This work proposes a complete set of optimization techniques that improve the performance of distributed ledger technology (DLT) systems over cloud storage environments under big data conditions and experimentally verifies several strategies’ efficiency in regard to storage and consistency. First, at the storage level, a compression optimization method manages to drastically reduce the occupied space by DLT data. Experimental results show that an appropriate compression ratio can improve storage efficiency and effectively manage access delay to maintain the overall responsiveness of the system. In terms of data consistency strategies, it uses an eventual consistency strategy that can be used to guarantee data reliability and consistency to achieve a DXGI-level throughput improvement. This approach will be able to satisfy the real-time responsiveness and accuracy of data of DLT systems in big data scenarios while further optimizing resource utilization. Furthermore, the comparison of several experimental results can show how different optimization strategies influence some key metrics, such as storage efficiency, access delay, throughput, and consistency, under different parameter settings. These findings serve both a theoretical and practical foundation for DLT systems that can work more efficiently in cloud environments. The result of this research carries high values in showing lessons for the future cloud storage optimization of DLT, and also lays a foundation for its wider application and promotion in big data-driven scenarios.
S. Nagaraju, Arpit Anil Panwar, Pavithra P, Rakhimjon Soataliyev · 6 authors
The rapid expansion of remote work has prompted organizations to seek cloud-based solutions that ensure data integrity, robust security, compliance with industry regulations, and optimal performance. Blockchain technology, recognized for its immutable distributed ledger capabilities, presents a compelling value proposition for securing remote workforce management platforms. This paper examines the integration of blockchain technology into cloud-based remote workforce management infrastructures, with a focus on data security, regulatory compliance, and performance optimization. Using hypothetical but realistic enterprise scenarios, real-time data metrics, and visual aids, we demonstrate how blockchain-backed cloud solutions can mitigate security breaches, streamline compliance reporting, and improve operational efficiency. The study concludes tshat blockchain’s decentralized verification mechanisms, combined with the scalability and reliability of modern cloud platforms, can significantly enhance the management of distributed teams while satisfying the evolving demands of data governance.
Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón · 5 authors
This paper evaluates the technical feasibility of blockchain technology within the healthcare ecosystem, with a focus on the use of Corda Distributed Ledger Technology (DLT) to ensure data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity—ensuring that transmitted data remains unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda Network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda blockchain technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.
Chenghuai Lu, Zhongyuan Yu, Guijuan Wang, Anming Dong · 5 authors
With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.
Ze-Peng Yang, Wenlong Tian, Emma Zhang, Zhiyong Xu
With the widespread adoption of cloud storage, effectively identifying and eliminating redundant data among users while ensuring data security has become a significant challenge. However, traditional similarity detection methods has limitations in privacy protection. Although conventional encryption techniques can safeguard privacy, they have difficulty detecting redundancy between similar blocks. Thus, we propose a secure reduction of redundant and similar data for cloud storage to address these challenges based on zero-knowledge proof (Sec-Reduce), called Sec-Reduce. It first employs a novel zero-knowledge proof technique for file-level redundancy detection, where redundant files are identified and excluded from storage. To further determine the similarity of non-redundant files, the scheme performs content-based chunking and feature extraction using a similarity feature extraction method. These extracted features are then encrypted using the approximate homomorphic encryption scheme Cheon-Kim-Kim-Song (CKKS) to enable similarity detection in the ciphertext environment. Finally, secure delta encoding is applied to store unique ciphertext blocks and deltas. Evaluations of real-world datasets demonstrate that Sec-Reduce achieves higher storage savings than existing encrypted storage methods, with storage overhead comparable to plaintext storage and only moderate performance overhead.
Poonam Kumari, Meeta Singh
Cloud computing, the fastest-growing IT technology, offers flexibility and scalability with pay-per-use models, but it raises concerns about data security due to third-party storage and online accessibility. Effective cloud security measures are crucial for protecting user data. The Internet of Things (IoT) generates enormous amounts of data that must be efficiently processed and stored in the cloud. Blockchain technology enhances data security through its tamper-resistant distributed ledger and peer-to-peer networks, which allow any node with internet access to join or create networks. To protect sensitive data and maintain its integrity, it is crucial to implement robust encryption, privacy safeguards, access controls, identity management and confidentiality in both cloud and decentralized systems.
Dongliang Cai, Liang Zhang, Borui Chen, Haibin Kan
Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization. Users generate their own public and secret keys and then register their keys and attributes with a transparent key curator. However, RABE still suffers from heavy decryption overhead. A natural approach to address this issue is to outsource decryption to a decryption cloud server (DCS). In this work, we propose the first auditable RABE scheme with reliable outsourced decryption (ORABE) based on blockchain. First, we achieve verifiability of transform ciphertext via a verifiable tag mechanism. Then, the exemptibility, which ensures that the DCS escapes false accusations, is guaranteed by zero knowledge fraud proof under the optimistic assumption. Additionally, our system achieves fairness and auditability to protect the interests of all parties through blockchain. Finally, we give concrete security and theoretical analysis and evaluate our scheme on Ethereum to demonstrate feasibility and efficiency.
R. Vijaya Saraswathi, M. Swathi, Sakshi Manoj Punwatkar, Shreya Konduru · 5 authors
This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies.
Jugnu Misal -
The integration of blockchain technology into automated incident management systems represents a significant advancement in securing and validating system logs and incident records. This article presents a comprehensive article analysis of blockchain's application in incident management, examining its role in creating immutable audit trails and enhancing security controls. Through systematic review of implementation patterns and industry case studies, the article explores how distributed ledger technology addresses traditional challenges in log integrity and incident response validation. The article investigates the architectural frameworks necessary for successful blockchain integration, including considerations for scalability, performance, and regulatory compliance. The findings demonstrate that blockchain-based incident management systems offer enhanced transparency, improved audit capabilities, and robust security measures compared to traditional approaches. Additionally, the article examines emerging patterns in enterprise adoption, implementation challenges, and the synergies between blockchain and other emerging technologies in the incident management landscape. This article contributes to the growing body of knowledge on blockchain applications in enterprise security operations and provides a framework for organizations considering blockchain adoption for their incident management processes. The article concludes with recommendations for implementation and identifies areas for future research in this rapidly evolving field.
Cai, Dongliang, Borui Chen, Liang Zhang, Haibin Kan
Attribute-based encryption (ABE) is a generalization of public-key encryption that enables fine-grained access control in cloud services. Recently, Hohenberger et al. (Eurocrypt 2023) introduced the notion of registered ABE, which is an ABE scheme without a trusted central authority. Instead, users generate their own public/secret keys and then register their keys and attributes with a key curator. The key curator is a transparent and untrusted entity and its behavior needs to be audited for malicious registration. In addition, pairing-based registered ABE still suffers the heavy decryption overhead like ABE. A general approach to address this issue is to outsource decryption to a decryption cloud service (DCS).In this work, we propose BA-ORABE, the first fully auditable registered ABE with reliable outsourced decryption scheme based on blockchain. First, we utilize a verifiable tag mechanism to achieve verifiability of ciphertext transformation, and the exemptibility which enables the honest DCS to escape from wrong claims is guaranteed by zero knowledge fraud proof under optimistic assumption. Additionally, our system achieves fairness and decentralized outsourcing to protect the interests of all parties and the registration and outsourcing process are transparent and fully auditable through blockchain. Finally, we give security analysis, implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, and show its advantages in real application of decentralized finance.
Sulyab Thottungal Valapu, Aritri Saha, Bhaskar Krishnamachari, Vivek Menon · 5 authors
In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.
Aparna Singh, Geetanjali Rathee
No abstract is available for this record.
Renas Bacho, Benedikt Wagner
No abstract is available for this record.
Emmanuel Syrmoudis, Stefan Mager, Jens Großklags
Lock-in practices of online services hinder consumers from switching frictionlessly to a competitor once they are unsatisfied with the company’s service offering, privacy practices, or philosophy. The right to data portability (RtDP) is one of the strongest measures introduced by recent privacy regulations to unlock continuously collected user data from centralized silos of market leaders. Introducing the obligation to provide means of data transfers between services, it aims to establish decentralized online markets and to foster competition. In this longitudinal study comprising a unique dataset of 129 online services over three consecutive years, we are the first to provide evidence on the development of the effectiveness of the EU’s RtDP. Astonishingly, only 16% of services could provide a compliant data export in all years, with services from the industries Entertainment and Travel performing worst. Overall, Finance & Insurance and Social Networks & Messaging include the services with the highest compliance rates. Regarding the usefulness of data portability, our analysis unveils that data export scope and data import options have stagnated between 2020 and 2022. Further, we are able to show that online services with a high presence of third-party trackers are less compliant and ready to export data from their systems. Lastly, our regression analyses show that service popularity significantly increases format compliance, export scope, and import options. This suggests that competitors to incumbents still perceive the regulation more as a bureaucratic burden than a unique opportunity to attract new consumers and their data.
Md. Mafiqul Islam, Dr Patrick Zingisa Msekelwa
Blockchain technology has emerged as a transformative tool for enhancing security, transparency, and scalability in distributed learning systems. This paper explores how blockchain can address critical challenges in these systems, such as data integrity, trust among participants, and scalability of learning models. By leveraging blockchain's decentralized ledger and smart contract capabilities, it becomes possible to establish secure data sharing, mitigate privacy concerns, and promote collaborative learning without reliance on central authorities. Furthermore, we discuss novel consensus mechanisms optimized for distributed learning, ensuring efficiency and scalability. Real-world applications in sectors like healthcare, finance, and education are also reviewed to highlight the practical benefits of integrating blockchain into distributed learning systems. Finally, the paper identifies open research challenges and future directions to advance this interdisciplinary field.
Yu‐Wen Cheng, Yijun Jing, Zicheng Shi, Chuan-Kun Wu
In recent years, protecting data privacy in distributed environments has become a key challenge with the rise of the Internet of Things (IoT) and edge computing. Federated learning helps mitigate the risk of data leakage by keeping data local, but issues such as authentication and insufficient privacy protection remain. In this paper, we propose a cloud-edge-end federated learning privacy protection scheme (FL-DPASS) that incorporates differential privacy and secret sharing. We introduce a Schnorr zero-knowledge proof authentication mechanism to secure end devices' access without revealing their identities. Additionally, we combine differential privacy and secret sharing to enhance security during model parameter transmission and aggregation. Experimental results demonstrate that our scheme strengthens data privacy protection while maintaining training efficiency and accuracy.