Preserving privacy in on-line analytical processing (OLAP)

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 dat...

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Bibliographic Details
Main Authors: WANG, Lingyu, JAJODIA, Sushil, WIJESEKERA, Duminda
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2007
Subjects:
Data warehouses
data analysis
information security
MITB student
Data Science
Information Security
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/5985
https://doi.org/10.1007/978-0-387-46274-5
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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