A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy
Keywords:
differential privacy, big data, access restriction, data privacy
Abstract
Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user2019;s privacy becomes a challenge. Lots of researches had been administered on securing big data, but still limited in efficient privacy management and data sensitivity. This study designed a big data framework named Big Data-ARpM that is secured and enforces privacy and access restriction level. The internal components of Big Data-ARpM consists of six modules. Data Pre-processor which contains a data cleaning component that checks each entity of the data for conformity.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2020-07-15
Issue
Section
License
Copyright (c) 2020 Authors and Global Journals Private Limited
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.