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

Abstract

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user 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