Preserving Privacy in On-Line Analytical Processing (OLAP) - Info and Reading Options
By Lingyu Wang, Sushil Jajodia and Duminda Wijesekera
"Preserving Privacy in On-Line Analytical Processing (OLAP)" was published by Springer in November 14, 2006, the book is classified in Computers genre, it has 180 pages and the language of the book is English.
“Preserving Privacy in On-Line Analytical Processing (OLAP)” Metadata:
- Title: ➤ Preserving Privacy in On-Line Analytical Processing (OLAP)
- Authors: Lingyu WangSushil JajodiaDuminda Wijesekera
- Language: English
- Number of Pages: 180
- Is Family Friendly: Yes - No Mature Content
- Publisher: Springer
- Publish Date: November 14, 2006
- Genres: Computers
“Preserving Privacy in On-Line Analytical Processing (OLAP)” Subjects and Themes:
- Subjects: ➤ OLAP technology - Computer security - Data protection - Data warehousing - Online data processing - Security measures
Edition Identifiers:
- Google Books ID: rUaRNQEACAAJ
- The Open Library ID: OL7445881M - OL18812223W
- Online Computer Library Center (OCLC) ID: 85443440
- Library of Congress Control Number (LCCN): 2006934196
- ISBN-13: 9780387462738
- ISBN-10: 0387462732
- All ISBNs: 0387462732 - 9780387462738
AI-generated Review of “Preserving Privacy in On-Line Analytical Processing (OLAP)”:
Snippets and Summary:
This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems.
"Preserving Privacy in On-Line Analytical Processing (OLAP)" Description:
Google Books:
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.
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