Security in Data Warehouses and OLAP Systems

Abstract

Unlike in operational databases, aggregation and derivation play a major role in on-line analytical processing (OLAP) systems and data warehouses. Unfortunately, the process of aggregation and derivation can also pose challenging security problems. Aggregated and derived data usually look innocent to traditional security mechanisms, such as access control, and yet such data may carry enough sensitive information to cause security breaches. This chapter ?rst demonstrates the security threat from aggregated and derived data in OLAP systems and warehouses. The chapter then reviews a series of methods for removing such a threat. Two efforts in extending existing inference control methods to the special setting of OLAP systems and warehouses are discussed. Both methods are not fully satisfactory due to limitations inherited from their counter parts in statistical databases. The chapter then reviews another solution based on a novel preventing-then-removing approach, which shows a promising direction towards securing OLAP systems and data warehouses.