This will be a graduate level seminar-style course introducing differential privacy and some of its applications. Differential privacy is a promising approach to the privacy-preserving release of data: it offers a strong guaranteed bound on the increase in harm that a user incurs as a result of participating in a differentially private data analysis. Several mechanisms and software tools have been developed to ensure differential privacy for a wide range of data analysis tasks, such as combinatorial optimization, machine learning, answering distributed queries, etc. In this class we will focus on some fundamental results about the theory and practice of differential privacy and how to use it in concrete applications. Part of the course will also focus on the use of differential privacy for purposes different from protecting privacy like for instance as a technique to prevent false discoveries in experimental science. The course will consist of readings on advanced topics in differential privacy and applications.
This will be seminar-style course where each student will present part of the material on differential privacy and applications. The class will be based on the book The Algorithmic Foundations of Differential Privacy (DR) and on the notes The Complexity of Differential Privacy (SV). Students are expected to read and comment the presented material previous to class by using NB. Every student will also be invited to engage on a project and to present the results at the end of the course. Discussion about all the aspects of the course will also take place on Piazza.
The grading will be based on the presentation of one paper, participation in class and in the NB and Piazza discussions. The project is optional and will not contribute to the grade.
The final grade will be composed as:
Projects can take different forms depending on the interest of each student but all the project must have a research component. Some examples of what would constitute a good project are:
|2/02|| Course Overview and Introduction to Differential Privacy
SV 1.1-1.4, DR 2.3.0, 3.2
| Marco Gaboardi
|2/09||Randomized Response revisited, Laplace Mechanism.
SV 1.5, DR 3.3
|2/16||Generalization of Laplace, examples, Report Noisy Max
|2/23||Exponential Mechanism, Composition.
DR 3.4, SV 1.6 (first paragraph), 2.2
|3/02||The Sparse Vector Technique
|Saurabh and Vidhi|
|3/09||Guest Lecture Slides||Georgios Kellaris|
|3/23||No class - Spring Break|
|3/30||Small DB algorithm
DR 4.1 - SV 4.1
|Akshay and Sucheta|
|4/06||Private Multiplicative Weights Algorithm
DR 4.2 - SV 4.2
|Saani and Vanshika|