Preserving Privacy in On-Line Analytical Processing (OLAP)

By: Lingyu Wang, Sushil Jajodia, Duminda Wijesekera

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Hardcover | 14 November 2006

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198 Pages

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23.5 x 15.88 x 1.91

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Preserving Privacy for On-Line Analytical Processing 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.

Preserving Privacy for On-Line Analytical Processing is appropriate for practitioners in industry as well as graduate-level students in computer science and engineering.

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Non-Fiction Computing & I.T.Databases Data Warehousing Computer Security Data Encryption Computer Networking & Communications Computer Science Business Applications Mathematical & Statistical Software Computer Programming & Software Development Algorithms & Data Structures

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Introduction	p. 1
Background	p. 1
Problem Statement	p. 4
Overview	p. 6
Overview of Basic Concepts	p. 7
Adapting Previous Approaches to Data Cubes	p. 8
A Lattice-based Solution	p. 10
OLAP and Data Cubes	p. 13
OLAP	p. 13
Data Cube	p. 15
Inference Control in Statistical Databases	p. 21
Query Set Size Control and Trackers	p. 23
The Star Query Model	p. 25
Key-Specified Queries	p. 26
Linear System Attack and Audit Expert	p. 28
Intractbility of Inference Control	p. 32
Inferences in Data Cubes	p. 37
Introduction	p. 37
Preliminaries	p. 38
Arbitrary Range Queries	p. 41
Restricted Range Queries	p. 45
Even Range Query Attack	p. 45
Indirect Even Range Query Attack	p. 48
Skeleton Query Attack	p. 49
Conclusion	p. 51
Cardinality-based Inference Control	p. 53
Introduction	p. 53
Preliminaries	p. 57
Data Cube	p. 57
Compromisability	p. 60
Formalization Rationale	p. 63
Cardinality-based Sufficient Conditions	p. 66
Trivial Compromisability	p. 66
Non-trivial Compromisability	p. 68
A Three-Tier Inference Control Model	p. 76
Cardinality-based Inference Control for Data Cubes	p. 80
Inference Control Algorithm	p. 80
Correctness and Time Complexity	p. 81
Implementation Issues	p. 83
Integrating Inference Control into OLAP	p. 83
Re-ordering Tuples in Unordered Dimensions	p. 84
Update Operations	p. 85
Aggregation Operators Other Than Sum	p. 86
Conclusions	p. 86
Parity-based Inference Control for Range Queries	p. 91
Introduction	p. 91
Preliminaries	p. 93
Motivating Examples	p. 93
Definitions	p. 94
Applying Existing Methods to MDR Queries	p. 97
Query Set Size Control, Overlap Size Control and Audit Expert	p. 97
Finding Maximal Safe Subsets of Unsafe MDR Queries	p. 99
Parity-Based Inference Control	p. 102
Even MDR Queries	p. 103
Characterizing the QDT Graph	p. 107
Beyond Even MDR Queries	p. 110
Unsafe Even MDR Queries	p. 112
Discussion	p. 114
Conclusion	p. 116
Lattice-based Inference Control in Data Cubes	p. 119
Introduction	p. 119
The Basic Model	p. 120
Specifying Authorization Objects in Data Cubes	p. 123
Controlling Inferences in Data Cubes	p. 126
Inferences in Data Cubes	p. 127
Preventing Multi-Dimensional Inferences	p. 130
Assumptions	p. 130
A Special Case	p. 131
The General Case	p. 136
Eliminating One-Dimensional Inferences	p. 140
Implementation Options and Complexity	p. 143
Summary	p. 145
Query-driven Inference Control in Data Cubes	p. 147
Introduction	p. 147
Authorization Objects and Queries in Data Cubes	p. 148
The Static Approach and Its Impact on Availability	p. 149
Query-Driven Prevention of Multi-Dimensional Inferences	p. 151
A Special Case	p. 152
The General Case	p. 156
Authorizing Queries	p. 160
Complexity Analysis	p. 165
Summary	p. 167
Conclusion and Future Direction	p. 169
References	p. 173
Index	p. 179
Table of Contents provided by Ingram. All Rights Reserved.

Preserving Privacy in On-Line Analytical Processing (OLAP)

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