TCS+ talk *this week*: Wednesday, October 23, Thomas Steinke, Google DeepMind
Clement Canonne
This is a reminder that the next TCS+ talk is taking place this week, Wednesday, October 23rd at 1:00 PM Eastern Time (10:00 AM Pacific Time, 19:00 Central European Time, 17:00 UTC). The speakers' slides will be made available at https://sites.google.com/view/tcsplus/welcome/past-talks before the talk starts.
If you’d like to join the Zoom talk, please sign up using the form at https://sites.google.com/view/tcsplus/welcome/next-tcs-talk. The talk will also be recorded and posted shortly afterwards on our YouTube channel, here: http://www.youtube.com/user/TCSplusSeminars.
Hoping to see you all there,
The organizers
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Speaker: Thomas Steinke (Google DeepMind)
Title: The Discrete Gaussian for Differential Privacy
Abstract: A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples from continuous distributions, and previous work has demonstrated that seemingly innocuous numerical errors can entirely destroy privacy. Moreover, when the underlying data is itself discrete (e.g., population counts), adding continuous noise makes the result less interpretable.
With these shortcomings in mind, we introduce and analyze the discrete Gaussian in the context of differential privacy. Specifically, we theoretically and experimentally show that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuous Gaussian noise. We also present an simple and efficient algorithm for exact sampling from this distribution. This demonstrates its applicability for privately answering counting queries, or more generally, low-sensitivity integer-valued queries.
Sumegha Garg
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