| Title | Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Cuzzocrea, Alfredo, Damiani, Ernesto |
| Conference Name | 2021 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | Advanced Tools and Systems for Big Data Processing, Big Data, Big Data Exchange, big data privacy, Biological system modeling, Conferences, Data integration, data privacy, Human Behavior, human factors, Metrics, Privacy-Preserving Big Data Exchange, pubcrawl, Resiliency, Scalability, smart cities, social networking (online), Theoretical Problems in Big Data |
| 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 constraintsf 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. |
| DOI | 10.1109/BigData52589.2021.9671686 |
| Citation Key | cuzzocrea_privacy-preserving_2021 |
Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions