<div dir="auto">This should be of interest to those of us who care about federated or transfer learning as well as privacy protection in AI.<div dir="auto"><br></div><div dir="auto">Artur</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">C3.ai Digital Transformation Institute</strong> <span dir="auto"><<a href="mailto:info@c3dti.ai" target="_blank" rel="noreferrer">info@c3dti.ai</a>></span><br>Date: Mon, Oct 17, 2022, 10:20 AM<br>Subject: Rachel Cummings on Improving Communication for Differential Privacy<br>To: <<a href="mailto:awd@cs.cmu.edu" target="_blank" rel="noreferrer">awd@cs.cmu.edu</a>><br></div><br><br><u></u>
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<h1 style="text-align:center;display:block;margin:0;padding:0;color:#202020;font-family:Arial,'Helvetica Neue',Helvetica,sans-serif;font-size:26px;font-style:normal;font-weight:bold;line-height:125%;letter-spacing:normal"><strong>Improving Communication for Differential Privacy: Insight from Human Behavior</strong></h1>
<p style="text-align:center;font-style:normal;font-weight:normal;margin:10px 0;padding:0;color:#202020;font-family:Helvetica;font-size:16px;line-height:150%"><strong><span style="font-size:16px">October 20, 1 pm PT/3 pm CT</span></strong><br>
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<strong>Rachel Cummings, Assistant Professor of Industrial Engineering and Operations Research, Columbia University</strong><br>
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<p dir="ltr" style="text-align:justify;margin:10px 0;padding:0;color:#202020;font-family:Helvetica;font-size:16px;line-height:150%">Differential privacy (DP) is widely regarded as a gold standard for privacy-preserving computation over users’ data. A key challenge is that the privacy guarantees are difficult to communicate to users, leaving them uncertain about how and whether they are protected. Despite recent widespread deployment of DP, relatively little is known about user perceptions and how to effectively communicate DP's practical privacy guarantees. This talk will cover a series of user studies aimed at measuring and improving communication with non-technical end users about DP. The first set explores users' privacy expectations related to DP and measures the efficacy of existing methods for communicating the privacy guarantees of DP systems. We find that the ways in which DP is described in-the-wild largely set users' privacy expectations haphazardly, which can be misleading depending on the deployment. Motivated by these findings, the second set develops and evaluates prototype descriptions designed to help end users understand DP guarantees. These descriptions target two important technical details in DP deployments that are often poorly communicated to end users: the privacy parameter epsilon (which governs the level of privacy protections) and distinctions between the local and central models of DP (which governs who can access exact user data).<br>
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<a href="https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=fb3f4dc3ae&e=b5ed94badf" style="color:#007c89;font-weight:normal;text-decoration:none" rel="noreferrer noreferrer" target="_blank">Rachel Cummings</a> is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University. Before joining Columbia, she was an Assistant Professor at the Georgia Institute of Technology. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. She is the recipient of an NSF CAREER Award, a DARPA Young Faculty Award, an Apple Privacy-Preserving Machine Learning Award, JP Morgan Chase Faculty Award, a Simons-Google Research Fellowship, a Mozilla Research Grant, and multiple best paper awards.</p>
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<div style="text-align:center"><em>The Science of Digital Transformation</em><br>
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University of Illinois at Urbana-Champaign<br>
1205 W. Clark Street, MC-257, Room 1008<br>
Urbana, Illinois 61801</font></span></p>
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