A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

Authors

  • Akinwunmi Oluwafemi

  • Onashoga S.A

  • Folorunso O.

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.

How to Cite

Akinwunmi Oluwafemi, Onashoga S.A, & Folorunso O. (2020). A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy. Global Journal of Computer Science and Technology, 20(E3), 65–75. Retrieved from https://computerresearch.org/index.php/computer/article/view/1983

A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

Published

2020-07-15