Personal data governance is became a key issue within organisations. This is mainly due to (i) the strategic value of personal data which provide more insights improving commercial and operational efficiency ; and (ii) data security risk issues and privacy regulation restrictions (e.g., GDPR and CCPA).
Creating data catalogs is an important step for setting up a personal data governance. However, it remains a time-consuming task especially because of the absence of naming conventions in database modeling coupled to the heterogeneity of database management systems (DBMS) across Information Systems (IS).
The paper presents SecP2I, an efficient data analytics-based approach permitting personal data discovery in structured and semi-structured datasets while guaranteeing end-to-end data confidentiality. The effectiveness of the platform is proven using a real world HR dataset.