Preserving privacy in on-line analytical processing (OLAP)
Publication Type
Book
Publication Date
1-2007
Abstract
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. The book 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.
Keywords
Data warehouses, data analysis, information security, MITB student
Discipline
Data Science | Information Security | Numerical Analysis and Scientific Computing
Volume
29
First Page
1
Last Page
180
ISBN
9780387462745
Identifier
10.1007/978-0-387-46274-5
Publisher
Springer
City or Country
Cham
Embargo Period
6-3-2021
Citation
WANG, Lingyu; JAJODIA, Sushil; and WIJESEKERA, Duminda.
Preserving privacy in on-line analytical processing (OLAP). (2007). 29, 1-180.
Available at: https://ink.library.smu.edu.sg/sis_research/5985
Additional URL
https://doi.org/10.1007/978-0-387-46274-5