Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints? The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions / A. Cuzzocrea, E. Damiani (... IEEE INTERNATIONAL CONFERENCE ON BIG DATA). - In: 2021 IEEE International Conference on Big Data (Big Data)New York : IEEE, 2021. - ISBN 978-1-6654-3902-2. - pp. 5081-5084 (( convegno IEEE International Conference on Big Data (Big Data) tenutosi a Orlando : 15-18 December nel 2021 [10.1109/BigData52589.2021.9671686].

Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions

E. Damiani
2021

Abstract

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints? The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.
Big Data Exchange; Privacy-Preserving Big Data Exchange; Theoretical Problems in Big Data; Advanced Tools and Systems for Big Data Processing
Settore INF/01 - Informatica
2021
IEEE
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/972872
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