Preserving Privacy in On-Line Analytical Processing (OLAP)
(Sprache: Englisch)
This book 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...
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This book 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.
Inhaltsverzeichnis zu „Preserving Privacy in On-Line Analytical Processing (OLAP) “
- Preface- Introduction
- 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
- References
- Index
Bibliographische Angaben
- Autoren: Lingyu Wang , Sushil Jajodia , Duminda Wijesekera
- 2006, 180 Seiten, Maße: 16,1 x 24,2 cm, Gebunden, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 0387462732
- ISBN-13: 9780387462738
Sprache:
Englisch
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