A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy - Akinwunmi Oluwafemi; Onashoga S.A; Folorunso O. (Global Journal of Computer Science and Technology, 2020)
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.