More and more data about the people are collected and analyzed. Typical data collection points include web browsers, social networks, wearable devices, and also governments. These data are stored, processed, and analyzed to better understand the behavior and make better predictions for science, medicine, advertising, and contact tracing. However, most data contain private information that should remain private and not be shared with others, such as medical history, political views, sexual orientation, locations visited, etc.

In this class, we study the questions on how to grant access to and even allow the computation on collected data without harming the privacy of individuals. This class introduces privacy-preserving data accessing techniques, such as private information retrieval and (group) oblivious RAM. The second part of the lecture covers techniques for privacy-preserving data analytics, such as k-anonymity, l-diversity, and differential privacy. These techniques have many applications and are used by companies like Apple and Google to collect and analyze data.