Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science

Chiusano, Silvia; Cerquitelli, Tania; Wrembel, Robert; Quercia, Daniele

doi:10.1007/s10796-020-10091-8

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…However, such DPGAN-based models are only suitable for centralized learning rather than distributed learning. Second, due to GDPR’s restrictions on data sharing strategies, data exists between hospitals in the form of “islands” (Liu et al 2020d ; Li et al 2020 ), which inspired researchers to develop a privacy-persevering distributed machine learning paradigm, i.e., Federated Learning, architecture to big data sources as well as to improve resource utilization and aggregate performance in shared environments (McMahan et al 2017 ; Chiusano et al 2021 ). In this context, applying privacy protection in distance learning systems was hot topic been discuss in Chiusano et al ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Second, due to GDPR’s restrictions on data sharing strategies, data exists between hospitals in the form of “islands” (Liu et al 2020d ; Li et al 2020 ), which inspired researchers to develop a privacy-persevering distributed machine learning paradigm, i.e., Federated Learning, architecture to big data sources as well as to improve resource utilization and aggregate performance in shared environments (McMahan et al 2017 ; Chiusano et al 2021 ). In this context, applying privacy protection in distance learning systems was hot topic been discuss in Chiusano et al ( 2021 ). Therefore, references (Li et al 2019 ; Ge et al 2020 ; Sheller et al 2020 ; Sui et al 2020 ) applied FL in medical fields to develop some privacy-persevering distance learning systems such as Medical Imaging (Li et al 2019 ), Medical Relation Extraction (Sui et al 2020 ), and Medical Named Entity Recognition (Ge et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

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Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

et al. 2021

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“…Also, attempts are often made to give a multifaceted definition of economic security [6]. The issues of the impact of innovations on national security are so widely presented in scientific works, due to the fact that the drivers of innovative development are digital economy technologies [7], which are immanently inherent in numerous risks (systemic; technological; regulatory) [3; 9].…”

Section: Methodsmentioning

confidence: 99%

Innovations in the System of Strategic Management of the Security of the National Economy

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2023

SHS Web Conf.

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The article is devoted to the study of the significance of innovations in the system of strategic management of the security of the national economy in the context of globalization and uncertainty. The strategically important tasks of the modern state are the development of domestic science-intensive production, the development, implementation, development of innovative information technologies focused on the production of competitive products. It is concluded that innovations in the conditions of an innovative economy are not only the final commercialized result of innovative activity that has received demand among individual consumers, but also intermediate results of the innovation process in the form of an innovative idea being implemented in the field of marketing, management, and personnel management. The article systematizes the attributive properties of innovations in the conditions of an innovative economy. The degree of entrepreneurial risk is analyzed due to the need to constantly search for new ways of doing business in a changing external environment and in conditions of high competition. The concepts of “innovative activity” and “innovative process” in the system of innovative economy are distinguished. The system of innovativeness of entrepreneurship in an innovative economy is generalized, if an entrepreneur at any specific stages of the life cycle seeks to introduce innovations, new technologies, innovative forms of personnel management or non-standard forms of interaction with competitors.

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“…Data Management refers to the collection, curation, and provision of data for AI implementation. Therefore, it is closely related to the data management capability from research on big data analytics, which is essential to the value creation from big data (Chiusano et al, 2021;Günther et al, 2017;Gupta & George, 2016). Similarly, data management in the AI context highlights the importance of providing appropriate data for building AI systems, as noted by prior research (Enholm et al, 2021;Jöhnk et al, 2021;Pumplun et al, 2019;Sjödin et al, 2021).…”

Section: Coping With Inscrutability and Data Dependency In Aimentioning

confidence: 98%

Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI

et al. 2022

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Artificial Intelligence (AI) implementation incorporates challenges that are unique to the context of AI, such as dealing with probabilistic outputs. To address these challenges, recent research suggests that organizations should develop specific capabilities for AI implementation. Currently, we lack a thorough understanding of how certain capabilities facilitate AI implementation. It remains unclear how they help organizations to cope with AI’s unique characteristics. To address this research gap, we employ a qualitative research approach and conduct 25 explorative interviews with experts on AI implementation. We derive four organizational capabilities for AI implementation: AI Project Planning and Co-Development help to cope with the inscrutability in AI, which complicates the planning of AI projects and communication between different stakeholders. Data Management and AI Model Lifecycle Management help to cope with the data dependency in AI, which challenges organizations to provide the proper data foundation and continuously adjust AI systems as the data evolves. We contribute to our understanding of the sociotechnical implications of AI’s characteristics and further develop the concept of organizational capabilities as an important success factor for AI implementation. For practice, we provide actionable recommendations to develop organizational capabilities for AI implementation.

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Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science

Cited by 14 publications

References 24 publications

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Innovations in the System of Strategic Management of the Security of the National Economy

Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI

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