Preserving Privacy in On-Line Analytical Processing (OLAP)

Lingyu Wang, Sushil Jajodia, Duminda Wijesekera (Autoren)

Buch | Hardcover

180 Seiten

2006
Springer-Verlag New York Inc.
978-0-387-46273-8 (ISBN)

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This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. It 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 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.

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.

Reihe/Serie	Advances in Information Security ; 29
Zusatzinfo	20 Illustrations, black and white; XII, 180 p. 20 illus.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
ISBN-10	0-387-46273-2 / 0387462732
ISBN-13	978-0-387-46273-8 / 9780387462738
Zustand	Neuware