Privacy-preserving OLAP against big query workloads: innovative theories and theorems

Focusing on the applicative setting represented by advanced big data analytics tools over data Cloud infrastructures, this paper introduces and theoretically proofs an innovative privacy-preserving OLAP framework against big query-workloads. The described applicative setting is common for big data analytics tools developed on top of commodity hardware solutions, where, rather than the execution of singleton big data queries, these queries are embedded into big data query programs, such that every elementary query evaluation step refers to one or more queries of the target query-workload. We complete our analytical and theoretical contributions by providing and experimentally assessing an innovative optimized approximate query answering algorithm for providing privacy-preserving approximate answers to OLAP queries.