Preserving Privacy in On-Line Analytical Processing (OLAP)
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Beschreibung
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.
Addresses the privacy issue of On-Line Analytic Processing systems
Details how to keep the performance overhead of these security methods at a reasonable level
Examines how a balance between security, availability, and performance can feasibly be achieved in OLAP systems
First book that concentrates solely on OLAP systems Includes Lattice-Based Inference Control Method Discusses methods that can be implemented on the basis of \emph(three-Tier) Inference control model in OLAP systems Includes supplementary material: sn.pub/extras
Autor*in
Lingyu Wang
Themen in »Preserving Privacy in On-Line Analytical Processing (OLAP)«
Analytical Jajodia OLAP On-line Preserving Privacy Processing Wang Wijesekera data warehouse database