Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy

This part focuses on privacy-preserving data analysis and explores privacy-enhanced technologies, with a particular emphasis on differential privacy.

We introduce the concept of privacy-preserving data analysis and delve into techniques and methodologies that allow for the analysis of data while protecting individuals' privacy.

We highlight the risks associated with reconstruction attacks in SQL, where an adversary attempts to reconstruct sensitive information from seemingly anonymized data. We discuss various prevention methods and countermeasures that can be employed to mitigate such attacks and protect individuals' privacy.

This part also provides an overview of privacy-enhanced technologies, such as differential privacy, federated learning, secure multiparty computation (SMC), and homomorphic encryption.

We provide an introduction to differential privacy...