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Computability, Zero-Knowledge & Differential Privacy →0/6
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1. Introduction, Finite Automata, Regular Expressions

Michael Sipser · 1:00:34

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2. Nondeterminism, Closure Properties, Conversion of Regular Expressions to FA

Michael Sipser · 1:03:27

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Lecture 1: Introduction to Cryptography by Christof Paar

Christof Paar · 1:17:24

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Proofs, Knowledge, and Computation: Zero-Knowledge Interactive Systems

Silvio Micali · 1:03:26

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What Needs to Be Added to Machine Learning? PAC Learning and Reasoning

Leslie G. Valiant · 54:36

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Differential Privacy: Fundamentals to Forefront

Cynthia Dwork · 59:33

Differential Privacy: Fundamentals to Forefront

Cynthia DworkSimons Institute (UC Berkeley)

Gordon McKay Professor of Computer Science, Harvard University

59:33·2015·graduate level·Research Seminar
Path step 6: Proves sharp mathematical bounds on statistical inference without leaking individual records.

Formalizes mathematical privacy guarantees under arbitrary auxiliary information, composition theorems, Laplace/Gaussian noise calibration, and adaptive data analysis.

Theory & AlgosCryptographySimons InstituteUC Berkeleycomputer science

Cynthia Dwork, Microsoft Research

Cryptography Boot Camp

Previous · Step 5

What Needs to Be Added to Machine Learning? PAC Learning and Reasoning

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