Personally Identifiable Data Field Checking Using Machine Learning

Abstract

Privacy impact assessments are the most frequently approach used by organizations to control privacy risks. However, how to automatically check PII data fields in PIA results is a big challenge. Manual verification of PIA results carried out by information security consultants is time consuming, costly and slow. In addition, detecting errors and anomalies in the data as their concentration wavers after a certain period of time. In this paper, we propose a methodology of checking PII data fields that can greatly reduce human errors and improve the quality of PIA reports.