De Aller-Bedste Bøger - over 12 mio. danske og engelske bøger
Levering: 1 - 2 hverdage

Preserving Privacy in On-Line Analytical Processing (Olap)

Bag om Preserving Privacy in On-Line Analytical Processing (Olap)

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.

Vis mere
  • Sprog:
  • Engelsk
  • ISBN:
  • 9780387462738
  • Indbinding:
  • Hardback
  • Sideantal:
  • 180
  • Udgivet:
  • 14. november 2006
  • Udgave:
  • 2007
  • Størrelse:
  • 161x16x242 mm.
  • Vægt:
  • 445 g.
  • 8-11 hverdage.
  • 7. december 2024
På lager

Normalpris

Abonnementspris

- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding

Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.

Beskrivelse af Preserving Privacy in On-Line Analytical Processing (Olap)

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.

Brugerbedømmelser af Preserving Privacy in On-Line Analytical Processing (Olap)