Preserving Privacy in On-Line Analytical Processing (OLAP)

Description

On-Line Analytic Processing (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. Existing inference control methods in statistical databases usually exhibit high performance overhead and limited effectiveness when applied to OLAP systems. Preserving Privacy in On-Line Analytical Processing reviews a series of methods that can precisely answer data cube-style OLAP queries regarding sensitive data while provably preventing adversaries from inferring the data. How to keep the performance overhead of these security methods at a reasonable level is also addressed. Achieving a balance between security, availability, and performance is shown to be feasible in OLAP systems. Preserving Privacy in On-Line Analytical Processing is designed for the professional market, composed of practitioners and researchers in industry. This book is also appropriate for graduate-level students in computer science and engineering. Front Matter....Pages I-XI Introduction....Pages 1-11 OLAP and Data Cubes....Pages 13-19 Inference Control in Statistical Databases....Pages 21-35 Inferences in Data Cubes....Pages 37-51 Cardinality-based Inference Control....Pages 53-89 Parity-based Inference Control for Range Queries....Pages 91-117 Lattice-based Inference Control in Data Cubes....Pages 119-145 Query-driven Inference Control in Data Cubes....Pages 147-167 Conclusion and Future Direction....Pages 169-171 Back Matter....Pages 173-180