[Theory] Re: FW: Theory Lunch 2023-05-10T17:30:00.000Z

[Antares Chen]

Hey everyone,

Just a reminder that theory lunch is again in *JCL 011* today!

We'll be resuming our regular location of JCL 298 next week.

See you all soon,
Antares

On Wed, May 10, 2023 at 12:29 AM Christopher Kang <ctkang at uchicago.edu>
wrote:

>
> ------------------------------
> *From:* noreply+automations at airtableemail.com <
> noreply+automations at airtableemail.com>On Behalf OfTheoryBot (via
> Airtable) <noreply+automations at airtableemail.com>
> *Sent:* Wednesday, May 10, 2023 12:29:32 AM (UTC-06:00) Central Time (US
> & Canada)
> *To:* Antares Chen <antaresc at uchicago.edu>
> *Cc:* Christopher Kang <ctkang at uchicago.edu>
> *Subject:* Theory Lunch 2023-05-10T17:30:00.000Z
>
> Today's Theory Lunch talk:
>
> *Christian Cianfarani (University of Chicago): From Differential Privacy
> to Distributional Privacy*
>
> https://uchicago.zoom.us/j/91616319229?pwd=dDdXQnFXeGNubFRkZy9hTDQrcWlXdz09
> <https://urldefense.com/v3/__https://uchicago.zoom.us/j/91616319229?pwd=dDdXQnFXeGNubFRkZy9hTDQrcWlXdz09__;!!BpyFHLRN4TMTrA!6hzjKcWTU46ShlucvPIzEwFKnnMmU78Z5gisG4ZOebewg_BIr18rqghRZUvWOObgMMi17z1TmwYt0G-SSEpFdRHPHsAWq3eXCmw$>
>
> Description: Differential privacy has gained widespread adoption as a
> framework for enforcing privacy guarantees when sharing information about
> datasets. However, these formal guarantees sometimes don’t line up with the
> kind of information we want to protect. In particular, differential privacy
> doesn’t protect the underlying distribution from which a dataset was drawn.
> In this talk, I will present the construction of differential privacy as
> well as a mechanism that can be used to enforce it, as originally defined
> by Dwork, McSherry, Nissim, and Smith. I will then illustrate a scenario in
> which sensitive information can be inferred using the outputs of a
> differentially-private mechanism. Finally, I will present a new framework,
> defined by Chen and Ohrimenko, that formalizes privacy guarantees for
> distributional information about datasets.
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