3.0

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This course provides an introduction to differential privacy, with a focus on algorithmic aspects (rather than statistical or engineering aspects). Specific topics we will cover include: motivation for differential privacy, and different versions of differential privacy (pure, approximate, Renyi, and zero-concentrated in particular); basic mechanisms (Laplace, Gaussian, Discrete Gaussian, and Exponential); composition theorems; basic algorithmic techniques (sparse vector technique, private multiplicative weights, private selection); beyond global sensitivity: local sensitivity, propose-test-release, subsampling; differentially private graph algorithms; lower bounds. Required Course Background: 601.433/633 or permission. Students may receive credit for only one of 601.438/638.