Reason from differential-privacy definitions to sensitivity and private statistical releases.
Build foundations in differential privacy, examine metrics related to local sensitivity, compare formal privacy definitions, and study private multi-party sketching for large-scale statistics. Emphasis is on neighboring-dataset assumptions, composition, utility, and the distinction between a mathematical guarantee and implementation evidence. Original study notes are not transcripts.
State the neighboring-dataset relation, privacy parameters, and randomized mechanism behind a DP claim. Explain how sensitivity and distance metrics influence noise required for a private release. Evaluate composition, utility, and threat-model assumptions in private statistical protocols.
Familiarity with probability, statistics, and basic algorithms is recommended. Experience with randomized mechanisms or security concepts is useful but not required; comfort with mathematical definitions is expected.
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Lesson 1 is free
4 lessons · Advanced · Full course.
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