Privacy-Preserving Machine Learning: Models, Algorithms and Implementations (Manning Early Access Program)
Prerequisites:
Theory of Probability and Statistics (basic)
Need to have some basic knowledge about probability and statistics, such as normal distribution, laplace distribution, combinations and permutations, etc..
Machine Learning (medium)
Need to know the basic machine learning concepts, such as supervised learning (e.g., classification), unsupervised learning (e.g., regression, clustering), and machine learning techniques, such as Support Vector Machine (SVM), Logistic Regression, Linear Regression, K-means, and neural networks, etc..
Python (medium)
Need to know the basic syntax of Python and how to write and debug Python code. The reader also need to be familiar with certain scientific computation and machine learning packages, such as NumPy, Scikit-learn, pyTorch, TensorFlow, etc..
Java (medium)
Need to know the basic syntax of Java, how to write and debug Java code.
Takeaways:
The reader will learn different privacy-preserving machine learning techniques, such as secure multiparty computation (MPC), compressive privacy, differential privacy (DP), local differential privacy (LDP), database security and privacy, etc.
The reader will learn how to implement and deploy different privacy-preserving machine learning techniques, such as differential private principal component analysis, locally differential private deep neural network, etc..
The reader will also learn and get an understanding of how to design a tailor-made privacy-preserving machine learning algorithm and their own privacy-preserving machine learning algorithms and systems by reading the show cases and projects in this book.