[Algorithmic Foundations] #1. Differential Privacy definition
I explain the concepts among the algorithmic foundations of differential privacy.
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NETS 4120: Algorithmic Game Theory #2 Differential Privacy
Mechanism Design via Differential Privacy
NETS 4120: Algorithmic Game Theory #1
Algorithmic Game Theory
[DP#3] Database anonymization – randomized response
Randomized response techniques have been developed and applied, allowing researchers to gather sensitive information without compromising respondent privacy or truthfulness.
[DP#2] Database anonymization – differential privacy
I explain the mathematical foundations and Laplacian noise of differential privacy.
[DP#1] Database anonymization – k-anonymity, l-diversity, t-closeness
I expand on the concept of k-anonymity and l-diversity with a further improvement called t-closeness.
Protected: Differential Privacy
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Protected: [review #11] Differential Privacy
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Hidden Markov Models & three key Problems and Algorithms
This is a compilation of a series of videos in which Prof. Patterson describes the Hidden Markov Model, starting with the Markov Model and proceeding to the three key questions for HMMs. A Hidden Markov Model is a machine learning model for predicting sequences of states from indirect observations.
Protected: [review #10] Combating Online Counterfeits: A Game-Theoretic Analysis of Cyber Supply Chain Ecosystem
There is no excerpt because this is a protected post.