ABSTRACT is 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. e 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 conrm to us the trend above is represented by the so-called perturbation-based techniques, which propose to alter the target data cube cell-by-cell to gain privacy-preserving query processing. is approach exposes us to clear limits, whose lack of extendibility and scalability is only the tip of an enormous iceberg. With the aim of fullling this critical drawback, this chapter describes and experimentally assesses a theoretically-sound accuracy/ privacy-constrained framework for computing privacy-preserving data cubes in OLAP environments. e benets derived from our proposed framework are twofold. First, we provide and meaningfully exploit solid theoretical foundations to the privacypreserving OLAP problem that pursue the idea of obtaining privacy-preserving data cubes via balancing accuracy and privacy of cubes by means of flexible sampling methods. Second, we ensure the efficiency and the scalability of the proposed approach, as conrmed to us by our experimental results, thanks to the idea of leaving the algorithmic vision of the privacy-preserving OLAP problem.