Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

Lugan, Sébastien; Desbordes, Paul; Brion, Eliott; Tormo, Luis Xavier Ramos; Legay, Axel; Macq, Benoı̂t

doi:10.1109/access.2019.2959220

Cited by 32 publications

(19 citation statements)

References 26 publications

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“…Similar works have been demonstrated by Kuo et al [45], [46] leveraging blockchain using Logistic Regression machine learning models. While these pipelines [13,32,33] overcome the risk of exposing the model weights, Lugan et al [47] proposed to train distributed learning models on encrypted data, preventing any exposure of local weights. Nevertheless, when implementing deep learning and encrypting model weights, model design requires careful consideration as aspects such as the CNN activation functions must be adapted [48].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

et al. 2020

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The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the data used to build models, and the confidence in the predictions they generate. Access to sufficient amounts of high-quality data to build accurate and reliable models remains problematic owing to substantive legal and ethical constraints in making clinically relevant research data available offsite. New technologies such as distributed learning offer a pathway forward, but unfortunately tend to suffer from a lack of transparency, which undermines trust in what data are used for the analysis. To address such issues, we hypothesized that, a novel distributed learning that combines sequential distributed learning with a blockchain-based platform, namely Chained Distributed Machine learning C-DistriM, would be feasible and would give a similar result as a standard centralized approach. C-DistriM enables health centers to dynamically participate in training distributed learning models. We demonstrate C-DistriM using the NSCLC-Radiomics open data to predict two-year lung-cancer survival. A comparison of the performance of this distributed solution, evaluated in six different scenarios, and the centralized approach, showed no statistically significant difference (AUCs between central and distributed models), all DeLong tests yielded p-val > 0.05. This methodology removes the need to blindly trust the computation in one specific server on a distributed learning network. This fusion of blockchain and distributed learning serves as a proof-of-concept to increase transparency, trust, and ultimately accelerate the adoption of AI in multicentric studies. We conclude that our blockchain-based model for sequential training on distributed datasets is a feasible approach, provides equivalent performance to the centralized approach.

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Section: Discussion and Future Workmentioning

confidence: 99%

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

et al. 2020

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“…[141][142][143][144] Furthermore, this approach can be coupled with other technologies such as blockchain to trace back data provenance and monitor the use of the final models. 145 Various techniques to visualise deep features have already been put forward by researchers to generate an intuitive understanding. A completely new research area of AI called explainable AI aims to track the decisions made by the intelligent algorithms so that it can be better understood by humans.…”

Section: Other Sites and Diseasesmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

225

130

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Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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“…Lugan et al [106] introduced a scalable security architecture by deriving a new paradigm of trusted coalitions with a high degree of trustworthiness which provides privacypreserving of data as well as motivation for coalition participation in the absence of a central authority. The proposed architecture is based on permissioned blockchains, which enable deep learning that is distributed with rising degrees of security and privacy.…”

Section: A Permissioned Blockchain-based Solutionsmentioning

confidence: 99%

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag¹,

et al. 2021

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In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

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Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

Cited by 32 publications

References 26 publications

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

Radiomics: from qualitative to quantitative imaging

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

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