[Theory] FW: Theory Lunch 2023-05-10T17:30:00.000Z
Christopher Kang
ctkang at uchicago.edu
Wed May 10 00:29:38 CDT 2023
________________________________
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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