Lingyu Wang, Sushil Jajodia, Duminda Wijesekera

Preserving Privacy in On-Line Analytical Processing (OLAP) (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer New York
• Seitenzahl: 180
• Erscheinungstermin: 6. April 2007
• Englisch
• ISBN-13: 9780387462745
• Artikelnr.: 37288591

Autorenporträt

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

Inhaltsangabe

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.