Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP Algorithms

A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAP

This paper complements the privacy preserving distributed OLAP framework proposed by us in a previous work by introducing four major theoretical properties that extend models and algorithms presented in the previous work, where the experimental validation of the framework has also been reported. Particularly, our framework makes use of the CUR matrix decomposition technique as the elementary co...

Experimental Analysis of Privacy-Preserving Statistics Computation

The recent investigation of privacy-preserving data mining and other kinds of privacy-preserving distributed computation has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. Building on the study of selective private function evaluation and the efforts towards practical algorithms for privacy-preserving data...

Experimental analysis of a privacy-preserving scalar product protocol

The recent investigation of privacy-preserving data mining has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. In an effort towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive: the privacy-preserving scalar product of two ve...

A Constraint-Based Framework for Computing Privacy Preserving OLAP Aggregations on Data Cubes

A constraint-based framework for computing privacy preserving OLAP aggregations on data cubes is proposed and experimentally assessed in this paper. Our framework introduces a novel privacy OLAP notion, which, following consolidated paradigms of OLAP research, looks at the privacy of aggregate patterns defined on multidimensional ranges rather than the privacy of individual tuples/data-cells, l...

Privacy Preserving OLAP and OLAP Security