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E-grāmata: Privacy Preserving Data Mining

  • Formāts: PDF+DRM
  • Sērija : Advances in Information Security 19
  • Izdošanas datums: 28-Sep-2006
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9780387294896
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  • Formāts: PDF+DRM
  • Sērija : Advances in Information Security 19
  • Izdošanas datums: 28-Sep-2006
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9780387294896
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Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense.Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area.Privacy Preserving Data Mining is designed for a professional audience composed of practitioners and researchers in industry. This volume is also suitable for graduate-level students in computer science.

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area.Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.
1 Privacy and Data Mining 1(6)
2 What is Privacy? 7(10)
2.1 Individual Identifiability
8(3)
2.2 Measuring the Intrusiveness of Disclosure
11(6)
3 Solution Approaches/Problems 17(12)
3.1 Data Partitioning Models
18(1)
3.2 Perturbation
19(2)
3.3 Secure Multi-party Computation
21(8)
3.3.1 Secure Circuit Evaluation
23(2)
3.3.2 Secure Sum
25(4)
4 Predictive Modeling for Classification 29(24)
4.1 Decision Tree Classification
31(3)
4.2 A Perturbation-Based Solution for ID3
34(4)
4.3 A Cryptographic Solution for ID3
38(2)
4.4 ID3 on Vertically Partitioned Data
40(5)
4.5 Bayesian Methods
45(6)
4.5.1 Horizontally Partitioned Data
47(1)
4.5.2 Vertically Partitioned Data
48(2)
4.5.3 Learning Bayesian Network Structure
50(1)
4.6 Summary
51(2)
5 Predictive Modeling for Regression 53(18)
5.1 Introduction and Case Study
53(7)
5.1.1 Case Study
55(1)
5.1.2 What are the Problems?
55(3)
5.1.3 Weak Secure Model
58(2)
5.2 Vertically Partitioned Data
60(8)
5.2.1 Secure Estimation of Regression Coefficients
60(2)
5.2.2 Diagnostics and Model Determination
62(1)
5.2.3 Security Analysis
63(2)
5.2.4 An Alternative: Secure Powell's Algorithm
65(3)
5.3 Horizontally Partitioned Data
68(1)
5.4 Summary and Future Research
69(2)
6 Finding Patterns and Rules (Association Rules) 71(14)
6.1 Randomization-based Approaches
72(7)
6.1.1 Randomization Operator
73(1)
6.1.2 Support Estimation and Algorithm
74(1)
6.1.3 Limiting Privacy Breach
75(3)
6.1.4 Other work
78(1)
6.2 Cryptography-based Approaches
79(3)
6.2.1 Horizontally Partitioned Data
79(1)
6.2.2 Vertically Partitioned Data
80(2)
6.3 Inference from Results
82(3)
7 Descriptive Modeling (Clustering, Outlier Detection) 85(28)
7.1 Clustering
86(5)
7.1.1 Data Perturbation for Clustering
86(5)
7.2 Cryptography-based Approaches
91(8)
7.2.1 EM-clustering for Horizontally Partitioned Data
91(4)
7.2.2 K-means Clustering for Vertically Partitioned Data
95(4)
7.3 Outlier Detection
99(14)
7.3.1 Distance-based Outliers
101(1)
7.3.2 Basic Approach
102(1)
7.3.3 Horizontally Partitioned Data
102(3)
7.3.4 Vertically Partitioned Data
105(1)
7.3.5 Modified Secure Comparison Protocol
106(1)
7.3.6 Security Analysis
107(3)
7.3.7 Computation and Communication Analysis
110(1)
7.3.8 Summary
111(2)
8 Future Research - Problems remaining 113(2)
References 115(6)
Index 121