Fwd: Rachel Cummings on Improving Communication for Differential Privacy

[Artur Dubrawski]

This should be of interest to those of us who care about federated or
transfer learning as well as privacy protection in AI.

Artur

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From: C3.ai Digital Transformation Institute <info at c3dti.ai>
Date: Mon, Oct 17, 2022, 10:20 AM
Subject: Rachel Cummings on Improving Communication for Differential Privacy
To: <awd at cs.cmu.edu>


Insight from Human Behavior
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The Colloquium on Digital Transformation is a series of weekly online talks
on how artificial intelligence, machine learning, and big data can lead to
scientific breakthroughs with large-scale societal benefit.


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*Improving Communication for Differential Privacy: Insight from Human
Behavior*

*October 20, 1 pm PT/3 pm CT*

*Rachel Cummings, Assistant Professor of Industrial Engineering and
Operations Research, Columbia University*

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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).

Rachel Cummings
<https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=fb3f4dc3ae&e=b5ed94badf>
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.


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