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<font face="Tahoma" size="2"><b>From:</b> noreply+automations@airtableemail.com <noreply+automations@airtableemail.com>On Behalf OfTheoryBot (via Airtable) <noreply+automations@airtableemail.com><br>
<b>Sent:</b> Wednesday, May 10, 2023 12:29:32 AM (UTC-06:00) Central Time (US & Canada)<br>
<b>To:</b> Antares Chen <antaresc@uchicago.edu><br>
<b>Cc:</b> Christopher Kang <ctkang@uchicago.edu><br>
<b>Subject:</b> Theory Lunch 2023-05-10T17:30:00.000Z<br>
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<p style="margin-top:0"><span>Today's Theory Lunch talk:</span></p>
<p><em><span>Christian Cianfarani (University of Chicago): From Differential Privacy to Distributional Privacy</span></em></p>
<p><span><a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/91616319229?pwd=dDdXQnFXeGNubFRkZy9hTDQrcWlXdz09__;!!BpyFHLRN4TMTrA!6hzjKcWTU46ShlucvPIzEwFKnnMmU78Z5gisG4ZOebewg_BIr18rqghRZUvWOObgMMi17z1TmwYt0G-SSEpFdRHPHsAWq3eXCmw$">https://uchicago.zoom.us/j/91616319229?pwd=dDdXQnFXeGNubFRkZy9hTDQrcWlXdz09</a></span></p>
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<p><span>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.</span></p>
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<div>©2023 Airtable</div>
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