In this course we will study individual privacy in data analysis from a rigorous theoretical perspective. We will focus on Differential Privacy: an approach to achieving strong provable privacy protection guarantees in the analysis of sensitive data. Informally, a data analysis algorithm is differentially private if changing the data of a single individual changes the output distribution of the algorithm only slightly. This guarantee ensures that the privacy risk to any individual increases only slightly by participating in data collection. Our focus in this course will be on the design of efficient differentially private algorithms. We will also learn about connections between differential privacy and other fields, such as statistics, machine learning, and geometry. While we focus on the algorithms, there will be proofs: it is important to prove formally that an algorithm provides privacy. Most proofs are simple, but the course does require mathematical maturity and a background in probability theory, and in the design and analysis of algorithms.eory, and in the design and analysis of algorithms.