This course is intended for students interested in data privacy, with a particular focus on differential privacy, and some applications. The course will introduce students to differential privacy which is becoming a standard approach to the privacy-preserving release of data. Differential privacy offers a strong guaranteed bound on the increase in harm that a user may incur as a result of participating in a differentially private data analysis. This course will focus on some fundamental results about the theory and practice of differential privacy and how to use it in concrete applications. In particular, the class will introduce students to the basic properties of differential privacy and to some of the fundamental mechanisms for building differentially private programs. Moreover, the course will present applications of differential privacy to a wide range of data analysis tasks, such as machine learning, answering distributed queries, statistical analysis and adaptive data analysis.
The course consists of a series of lectures on different topics in differential privacy and applications divided in two main parts. The first part of the class will focus on the basic methods to achieve differential privacy: methods based on global sensitivity, composition schemes for differential privacy, methods that use alternatives to global sensitivity, methods based on correlated noise. The second part of the class will focus on different models: differential privacy in the streaming model, multiparty models for differential privacy and some relations of differential privacy with complexity, statistics, machine learning, and adaptive data analysis.
The course has a significant component based on analysis of algorithms, so CSE 531 is a prerequisite. Additionally, some rudimentary understanding of probability and statistics is expected.
The course has two main requirements:
The grading will be based on assignments, participation in class, and project.
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:
| Date | Topic | Notes |
|---|---|---|
| M 8/28 | Course Overview and Introduction to Differential Privacy |
Slides |
| W 8/30 | Counting Queries and Reconstruction attacks SV 1.3, DR 8.1 |
Slides |
| M 9/04 | Labor Day - No Class |
|
| W 9/6 | No Class |
|
| M 9/11 | Definition of Differential Privacy DR 2, SV 1.4 |
Slides |
| W 9/13 | Randomized Response DR 3.2, SV 1.5 |
Slides |
| M 9/18 | Laplace Mechanism, Resilience to Post-Processing, Group
Privacy DR 2.3 - 3.3, SV 1.5, 2.1 |
Slides |
| W 9/20 | Basic Composition DR 3.5, SV 2.2, 2.3 |
Slides |
| M 9/25 | Histograms, Report Noisy Max DR 3.3, SV 2.3 |
Slides |
| W 9/27 | The Exponential Mechanism DR 3.4 |
Slides |
| M 10/02 | The Sparse Vector technique DR 3.6 |
Slides |
| W 10/04 | The Sparse Vector technique accuracy DR 3.6 |
Slides |
| M 10/09 | The SmallDB algorithm DR 4.1 |
Slides |
| W 10/11 | The MWEM algorithm DR 4.2 |
The slides are included in the ones for next class. |
| M 10/16 | The MWEM algorithm, The DualQuery algorithm DR 4.2 |
Slides |
| W 10/18 | DP vs Blatantly non-privacy DR 4.2 |
Slides |
| M 10/23 | Adaptive MWEM, DualQuery DR 4.2 SV 4.2 |
Slides |
| W 10/25 | Advanced Composition SV 2.2 |
Slides |
| M 10/30 | No Class |
|
| W 11/01 | Gaussian Mechanism DR Appendix A |
Slides |
| M 11/06 | zero-Concentrated Differential Privacy, Renyi Differential Privacy |
Slides |
| W 11/08 | Multiparty DP SV 9.1-9.2 |
Slides |
| M 11/13 | Multiparty DP SV 9.1-9.3 |
Slides |
| W 11/15 | Alternatives to Global Sensitivity SV 3 |
Slides |
| M 11/20 | Alternatives to Global Sensitivity SV 3 |
Slides |
| W 11/22 | Thanksgiving - No Class |
|
| M 11/27 | Alternatives to Global Sensitivity SV 3 |
Slides |
| W 11/29 | ||
| M 12/04 | ||
| W 12/06 | Project Presentations (10am-1pm) |
|