Multi-client Cloud-based Symmetric Searchable Encryption

Kermanshahi, Shabnam Kasra; Liu, Joseph K.; Steinfeld, Ron; Nepal, Surya; Lai, Shangqi; Loh, Randolph; Zuo, Cong

doi:10.1109/tdsc.2019.2950934

Cited by 25 publications

(10 citation statements)

References 35 publications

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“…The framework incorporates robust consensus mechanisms and smart contracts to facilitate trust among participants and establish a fair reward system. This incentivizes active participation and encourages data contribution, leading to a more comprehensive and diverse dataset for training models [37].…”

Section: Consensus Mechanisms and Smart Contractsmentioning

confidence: 99%

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Ali,

Al-rimy,

Tin

et al. 2023

Sensors

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Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain’s immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.

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Section: Consensus Mechanisms and Smart Contractsmentioning

confidence: 99%

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Ali,

Al-rimy,

Tin

et al. 2023

Sensors

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“…The existing authentication and key agreement methods face challenges such as vulnerability to cyber attacks, lack of scalability, and inefficient resource utilization. Additionally, the integration of blockchain technology into digital healthcare systems introduces new complexities that need to be addressed [27].…”

Section: Problem Statementmentioning

confidence: 99%

Revolutionizing Digital Healthcare: Unlocking the Power of Blockchain with an Optimized Fuzzy Logic Approach to Authentication and Key Agreement

Ali,

Tin,

Al-rimy

et al. 2023

Preprint

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Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover, the integration of blockchain technology provides a decentralized and tamper-proof infrastructure for securely storing and managing authentication and key agreement data. This ensures transparency and trust in the system, mitigating the risks of unauthorized access and data manipulation. The blockchain-based architecture also enables efficient resource utilization and scalability, allowing the system to handle authentication requests in a timely manner, even in large-scale digital healthcare environments.The proposed method is evaluated by using the NIST Special Database 302 and it shows superior performance compared to existing methods, with minimum False Rejection Rate (FRR), False Acceptance Rate (FAR), and response time. Moreover, the proposed method minimizes communication overhead during the authentication process and resists different cyber attacks including a Replay attack, Man-in-the-middle attack, Denial of Service (DoS) attack, and Impersonation attack. The proposed method achieves excellent performance in terms of security, efficiency, and resistance to various cyber-attacks, making it a promising approach for secure data sharing in P2P cloud environments.

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“…Its core idea is to determine the probability and expectation of the nodes to obtain the correct accounting based on the proportion of certain key resources owned by the nodes, so as to improve the security of the public chain network. Kermanshahi et al [ 26 ] realized the design of a bitcoin system based on the traditional Proof of Work (PoW), and the blockchain was proposed for the first time as its underlying technology. Kermanshahi et al [ 27 ] proposed Proof of Stake and introduced the concept of age of currency for the first time.…”

Section: Background and Related Studiesmentioning

confidence: 99%

“…In Cloud Block, several providers offer various types of data centers. In the proposed architecture, three data centers are used: A, B, and C. To create a blockchain network, the 268 proposed GTBSS-HDNN architecture is implemented at each data center and these are the 269 entities in the Cloud Block network [ 26 ]. The trust between the verifiable, auditable, 270, and immutable blocks is built using the proposed hybrid NN architecture.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

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The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

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Multi-client Cloud-based Symmetric Searchable Encryption

Cited by 25 publications

References 35 publications

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Revolutionizing Digital Healthcare: Unlocking the Power of Blockchain with an Optimized Fuzzy Logic Approach to Authentication and Key Agreement

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

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