1 Privacy considerations in machine learning

This chapter covers

The importance of privacy protection in the era of big data artificial intelligence
Types of privacy-related threats, vulnerabilities, and attacks in machine learning
Techniques that can be utilized in machine learning tasks to minimize or evade privacy risks and attacks

Our search queries, browsing history, purchase transactions, watched videos, and movie preferences are a few types of information that are collected and stored daily. Advances in artificial intelligence have increased the ability to capitalize on and benefit from the collection of private data.

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

1.4.1 Use of differential privacy

Summary