CS 461 - Topics in Data Privacy

Credits: 3

This course will explore both threats to privacy and solutions to the data privacy problem. In the first half of the course, we will demonstrate that traditional approaches to protecting privacy, such as anonymization, are subject to powerful attacks that reveal individuals? sensitive data. We will cover more recent approaches for protecting privacy, including k-anonymity and l-diversity. We will also discuss a variety of privacy enhancing technologies such as secure multi-party computation, zero-knowledge proofs, homomorphic encryption and Tor. The second half of the course will focus on the current de facto standard for data privacy?differential privacy (DP). We will cover fundamentals of DP, including its formal definitions, composition theorems and basic algorithms to satisfy DP. We will also cover the local model of DP which is used by industry to collect sensitive data from users. Time permitting, we will cover more advanced applications of DP including synthetic data generation and machine learning on private data. Coursework will include implementing privacy algorithms and running simulations using popular languages for data science such as Python and Julia. Prior background is not assumed. Prerequisite: Familiarity with Python programming, CS 375 Algorithms, Probability and Statistics: MATH 327 or MATH 448; or equivalent. Expected to be offered once a year.