Preserving Privacy in On-Line Analytical Processing (OLAP)

by Lingyu Wang, Sushil Jajodia, Duminda Wijesekera

Overview

Preserving Privacy for On-Line Analytical Processing addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. This volume reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data.

Preserving Privacy for On-Line Analytical Processing is appropriate for practitioners in industry as well as graduate-level students in computer science and engineering.

Product Details

ISBN-13:	9780387462738
Publisher:	Springer US
Publication date:	11/14/2006
Series:	Advances in Information Security , #29
Edition description:	2007
Pages:	180
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

OLAP and Data Cubes.- Inference Control in Statistical Databases.- Inferences in Data Cubes.- Cardinality-based Inference Control.- Parity-based Inference Control for Range Queries.- Lattice-based Inference Control in Data Cubes.- Query-driven Inference Control in Data Cubes.- Conclusion and Future Direction.