Главная
Study mode:
on
1
Intro
2
Local and Central Differential Privacy
3
QA
4
Stochastic convex optimization
5
New algorithms
6
Nonsmooth case
7
Whats missing
8
Interaction is necessary
9
Learning settings
10
Questions
11
Committee Selection
12
Applications
13
Stable Committees
14
Question Time
Description:
Explore the critical concepts of privacy and fairness in computing through this 45-minute ACM conference talk. Delve into local and central differential privacy, stochastic convex optimization, and new algorithms for addressing privacy concerns. Examine the nonsmooth case, identify gaps in current approaches, and understand why interaction is necessary in privacy-preserving systems. Investigate various learning settings and their implications for privacy. Discover practical applications, including committee selection and stable committees, and engage in a thought-provoking question-and-answer session to deepen your understanding of these crucial topics in modern computing.

Privacy and Fairness

Association for Computing Machinery (ACM)
Add to list
0:00 / 0:00