A content-based data masking technique for a built-in framework in Business Intelligence platform

摘要

The implementation of a Business Intelligence (BI) platform in different types of organizations including healthcare, has become an important project. One common implementation is to extract sensitive data from production databases and then load them into an enterprise Data Warehouse (DW). However, the internal privacy breach that occurs when developers, researchers, and testers access the DW is a serious threat that should be taken into consideration. Reversible data masking techniques are used to de-identify the sensitive data, preserve the data format, and maintain the quality of data utility (data analytics). In this paper a practical built-in data masking framework (IMETUIdentify, Map, Execute, Test, and Utilize) is proposed focusing on the execution and testing modules. The proposed data masking algorithm is derived from the statistical content of the extracted dataset, which is grouped at certain levels (micro-aggregation) that are associated with a numeric attribute. The combination of the related statistical information will be used within a mathematical formula to generate the new masked value, and then the statistical variables will be put together in such a sequence and encapsulated to form a strong pair of public and secret keys. This strengthens the security factor while introducing small overheads in performance and space in comparison with native encryption techniques.