The Case of OLAP
This chapter explores the emerging context of privacy-preserving OLAP over Big Data, a novel topic that is playing a critical role in actual Big Data research, and proposes an innovative framework for supporting intelligent techniques for computing privacy-preserving OLAP aggregations on data cubes. The proposed framework originates from the evidence stating that state-of-the-art privacy-preserving OLAP approaches lack strong theoretical bases that provide solid foundations to them. In other words, there is not a theory underlying such approaches, but rather an algorithmic vision of the problem. A class of methods that clearly confirm to us the trend ...