This EM is about an application of probability in (differential) privacy.
The whole field of differential privacy is about the tradeoff between accuracy (in publishing statistics from a survey/census, or more generally, aggregating data) and privacy (releasing or leaking information about individuals in the data).
Broadly speaking, privacy appears in many downstream courses offered by the department (though not all of them models privacy using the differential privacy regime):
- CS333 (Algorithms in the Real World)
- Anything Prof. Pardis Emami-Naeini teaches (CS290/586)
- Privacy-focused grad course in the past (Shao-Heng took this in Fall 2021 and that’s where he learned about differential privacy)
Concepts used in EMB:
- Conditional probability and expectation from Core Module 8: Graph Fundamentals Part I-II and Core Module 8: Graph Fundamentals Part III
- Sets and binary relations from Core Module 4: Sets, Functions, and Relations
To earn a satisfactory completion for EMB:
- Complete Assignment (individually or in pairs).
- You should submit by LDoC to ensure that you get at least one round of feedback.
- You can keep submitting until 5/1 11:59pm.
