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Privacy-preserving big data analytics a comprehensive survey.
- Source :
-
Journal of Parallel & Distributed Computing . Dec2019, Vol. 134, p207-218. 12p. - Publication Year :
- 2019
-
Abstract
- In this paper, we present a comprehensive survey of privacy-preserving big data analytics. We introduce well-designed taxonomies which offer both systematic views and a detailed classification of this challenging research field. We give insights into recent studies on existing active topics in the field. Furthermore, we identify open future research directions for privacy-preserving big data analytics. This survey can serve as a good reference source for the development of modern privacy-preserving techniques to address various privacy-related scenarios to be encountered in practice. • A comprehensive survey of privacy-preserving big data analytics. • Well-designed taxonomies with both systematic views and detailed classification. • Insights into recent works on existing active privacy topics in social networks. • Open future research directions for privacy-preserving big data analytics. • A good reference source to address various privacy-related scenarios. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BIG data
*DATA privacy
*REFERENCE sources
*INTERNET privacy
*SOCIAL networks
Subjects
Details
- Language :
- English
- ISSN :
- 07437315
- Volume :
- 134
- Database :
- Academic Search Index
- Journal :
- Journal of Parallel & Distributed Computing
- Publication Type :
- Academic Journal
- Accession number :
- 138890332
- Full Text :
- https://doi.org/10.1016/j.jpdc.2019.08.007