Privacy Preserving Machine Learning MEAP Version 8 1st Edition by Morris Chang, Di Zhuang, G Dumindu Samaraweera ISBN 9781617298042 1617298042

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Product details:
ISBN 10: 1617298042
ISBN 13: 9781617298042
Author: Morris Chang, Di Zhuang, G Dumindu Samaraweera

Privacy Preserving Machine Learning MEAP Version 8 1st Edition Table of contents:

Part 1 Basics of privacy-preserving machine learning with differential privacy

1 Privacy considerations in machine learning

1.1 Privacy complications in the AI era

1.2 The threat of learning beyond the intended purpose

1.2.1 Use of private data on the fly

1.2.2 How data is processed inside ML algorithms

1.2.3 Why privacy protection in ML is important

1.2.4 Regulatory requirements and the utility vs. privacy tradeoff

1.3 Threats and attacks for ML systems

1.3.1 The problem of private data in the clear

1.3.2 Reconstruction attacks

1.3.3 Model inversion attacks

1.3.4 Membership inference attacks

1.3.5 De-anonymization or re-identification attacks

1.3.6 Challenges of privacy protection in big data analytics

1.4 Securing privacy while learning from data: Privacy-preserving machine learning

1.4.1 Use of differential privacy

1.4.2 Local differential privacy

1.4.3 Privacy-preserving synthetic data generation

1.4.4 Privacy-preserving data mining techniques

1.4.5 Compressive privacy

1.5 How is this book structured?

Summary

2 Differential privacy for machine learning

2.1 What is differential privacy?

2.1.1 The concept of differential privacy

2.1.2 How differential privacy works

2.2 Mechanisms of differential privacy

2.2.1 Binary mechanism (randomized response)

2.2.2 Laplace mechanism

2.2.3 Exponential mechanism

2.3 Properties of differential privacy

2.3.1 Postprocessing property of differential privacy

2.3.2 Group privacy property of differential privacy

2.3.3 Composition properties of differential privacy

Summary

3 Advanced concepts of differential privacy for machine learning

3.1 Applying differential privacy in machine learning

3.1.1 Input perturbation

3.1.2 Algorithm perturbation

3.1.3 Output perturbation

3.1.4 Objective perturbation

3.2 Differentially private supervised learning algorithms

3.2.1 Differentially private naive Bayes classification

3.2.2 Differentially private logistic regression

3.2.3 Differentially private linear regression

3.3 Differentially private unsupervised learning algorithms

3.3.1 Differentially private k-means clustering

3.4 Case study: Differentially private principal component analysis

3.4.1 The privacy of PCA over horizontally partitioned data

3.4.2 Designing differentially private PCA over horizontally partitioned data

3.4.3 Experimentally evaluating the performance of the protocol

Summary

Part 2 Local differential privacy and synthetic data generation

4 Local differential privacy for machine learning

4.1 What is local differential privacy?

4.1.1 The concept of local differential privacy

4.1.2 Randomized response for local differential privacy

4.2 The mechanisms of local differential privacy

4.2.1 Direct encoding

4.2.2 Histogram encoding

4.2.3 Unary encoding

Summary

5 Advanced LDP mechanisms for machine learning

5.1 A quick recap of local differential privacy

5.2 Advanced LDP mechanisms

5.2.1 The Laplace mechanism for LDP

5.2.2 Duchi’s mechanism for LDP

5.2.3 The Piecewise mechanism for LDP

5.3 A case study implementing LDP naive Bayes classification

5.3.1 Using naive Bayes with ML classification

5.3.2 Using LDP naive Bayes with discrete features

5.3.3 Using LDP naive Bayes with continuous features

5.3.4 Evaluating the performance of different LDP protocols

Summary

6 Privacy-preserving synthetic data generation

6.1 Overview of synthetic data generation

6.1.1 What is synthetic data? Why is it important?

6.1.2 Application aspects of using synthetic data for privacy preservation

6.1.3 Generating synthetic data

6.2 Assuring privacy via data anonymization

6.2.1 Private information sharing vs. privacy concerns

6.2.2 Using k-anonymity against re-identification attacks

6.2.3 Anonymization beyond k-anonymity

6.3 DP for privacy-preserving synthetic data generation

6.3.1 DP synthetic histogram representation generation

6.3.2 DP synthetic tabular data generation

6.3.3 DP synthetic multi-marginal data generation

6.4 Case study on private synthetic data release via feature-level micro-aggregation

6.4.1 Using hierarchical clustering and micro-aggregation

6.4.2 Generating synthetic data

6.4.3 Evaluating the performance of the generated synthetic data

Summary

Part 3 Building privacy-assured machine learning applications

7 Privacy-preserving data mining techniques

7.1 The importance of privacy preservation in data mining and management

7.2 Privacy protection in data processing and mining

7.2.1 What is data mining and how is it used?

7.2.2 Consequences of privacy regulatory requirements

7.3 Protecting privacy by modifying the input

7.3.1 Applications and limitations

7.4 Protecting privacy when publishing data

7.4.1 Implementing data sanitization operations in Python

7.4.2 k-anonymity

7.4.3 Implementing k-anonymity in Python

Summary

8 Privacy-preserving data management and operations

8.1 A quick recap of privacy protection in data processing and mining

8.2 Privacy protection beyond k-anonymity

8.2.1 l-diversity

8.2.2 t-closeness

8.2.3 Implementing privacy models with Python

8.3 Protecting privacy by modifying the data mining output

8.3.1 Association rule hiding

8.3.2 Reducing the accuracy of data mining operations

8.3.3 Inference control in statistical databases

8.4 Privacy protection in data management systems

8.4.1 Database security and privacy: Threats and vulnerabilities

8.4.2 How likely is a modern database system to leak private information?

8.4.3 Attacks on database systems

8.4.4 Privacy-preserving techniques in statistical database systems

8.4.5 What to consider when designing a customizable privacy-preserving database system

Summary

9 Compressive privacy for machine learning

9.1 Introducing compressive privacy

9.2 The mechanisms of compressive privacy

9.2.1 Principal component analysis (PCA)

9.2.2 Other dimensionality reduction methods

9.3 Using compressive privacy for ML applications

9.3.1 Implementing compressive privacy

9.3.2 The accuracy of the utility task

9.3.3 The effect of ρ’ in DCA for privacy and utility

9.4 Case study: Privacy-preserving PCA and DCA on horizontally partitioned data

9.4.1 Achieving privacy preservation on horizontally partitioned data

9.4.2 Recapping dimensionality reduction approaches

9.4.3 Using additive homomorphic encryption

9.4.4 Overview of the proposed approach

9.4.5 How privacy-preserving computation works

9.4.6 Evaluating the efficiency and accuracy of the privacy-preserving PCA and DCA

Summary

10 Putting it all together: Designing a privacy-enhanced platform (DataHub)

10.1 The significance of a research data protection and sharing platform

10.1.1 The motivation behind the DataHub platform

10.1.2 DataHub’s important features

10.2 Understanding the research collaboration workspace

10.2.1 The architectural design

10.2.2 Blending different trust models

10.2.3 Configuring access control mechanisms

10.3 Integrating privacy and security technologies into DataHub

10.3.1 Data storage with a cloud-based secure NoSQL database

10.3.2 Privacy-preserving data collection with local differential privacy

10.3.3 Privacy-preserving machine learning

10.3.4 Privacy-preserving query processing

10.3.5 Using synthetic data generation in the DataHub platform

Summary

Appendix A. More details about differential privacy

A.1 The formal definition of differential privacy

A.2 Other differential privacy mechanisms

A.2.1 Geometric mechanism

A.2.2 Gaussian mechanism

A.2.3 Staircase mechanism

A.2.4 Vector mechanism

A.2.5 Wishart mechanism

A.3 Formal definitions of composition properties of DP

A.3.1 The formal definition of sequential composition DP

A.3.2 The formal definition of parallel composition DP

references

Appendix

index

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Tags: Morris Chang, Di Zhuang, G Dumindu Samaraweera, Privacy Preserving, Machine Learning