Differential Privacy

topic on 3 shows · 5 statements across 5 episodes

the Y Combinator Startup Podcast the MAD Podcast the a16z Podcast

5 statements about Differential Privacy, every show

MAD Assertion Not checkable as stated
Differential privacy fails on fine-grained analysis like genome-wide association studies
“The challenge with that, it actually doesn't work very well if you need really fine-grained analysis as you would, for example genome-wide association studies.”
Kurt Rohloff Oct 22, 2019 ▶ 15:37 Computing On Encrypted Data // Kurt Rohloff, Duality (Firstmark's Data Driven NYC)
a16z Assertion Supported
Chen: Differential privacy guarantees inherently reduce AI prediction accuracy
“So there's a trade-off between how much security you can guarantee and then the accuracy of their predictions.”
Frank Chen Jan 2, 2019 ▶ 7:54 a16z Podcast | Apple and the Widgetification of Everything
Y COMBINATOR Assertion Supported
Aaronson: Differential privacy and quantum shadow tomography share a mathematical connection
“There's a, you know, precise mathematical connection between these two problems. You can prove it. You know, it goes in both directions, and then we were actually able to use it to, you know, take work that's been done in differential privacy by people who don…”
Scott Aaronson Jun 29, 2018 ▶ 29:46 Scott Aaronson on Computational Complexity Theory and Quantum Computers · Y Combinator
a16z Assertion Not checkable as stated
Jordan argues differential privacy is rarely analyzed for statistical inference
“Differential privacy has mostly not been thought about inferentially”
Michael Jordan Jul 28, 2017 ▶ 7:12 Michael Jordan
a16z Assertion Supported
Formal proof of differential privacy implementations remains unsolved, says Kathryn McKinley
“And so we've come, we've taken some good steps in this direction. But we haven't done the very hardest step, which is to prove in the implementation of differential privacy that you actually get it.”
Kathryn McKinley Jul 28, 2017 ▶ 15:25 Kathryn McKinley

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