[Busec] Fwd: Talk by Kamalika Chaudhuri, October 10, 2:00 in Hariri
goldbe at cs.bu.edu
Wed Oct 8 19:12:05 EDT 2014
FYI: A talk on differential privacy at BU CS's theory seminar, this Friday.
---------- Forwarded message ----------
From: Steve Homer <homer at cs.bu.edu>
Date: Wed, Oct 8, 2014 at 5:41 PM
Subject: Talk by Kamalika Chaudhuri, October 10, 2:00 in Hariri
To: Steve Homer <homer at cs.bu.edu>
Cc: Nora Conroy <conroynm at cs.bu.edu>
Boston University Friday Theory Seminar
Kamalika Chaudhuri on October 10, 2:00
On Friday, October 10 at 2:00, Kamalika Chaudhuri (UCSD)
will speak on, "Challenges in Differentially-Private Data Analysis."
The talk is in the Hariri Inisitute Conference Room (Rm 180) at 111
Refreshments at 1:50
Machine learning algorithms increasingly work with sensitive information on
individuals, and hence the problem of privacy-preserving data analysis --
how to design data analysis algorithms that operate on the sensitive data
of individuals while still guaranteeing the privacy of individuals in
the data-- has achieved great practical importance. In this
talk, we address two problems in differentially private data analysis.
First, we address the problem of privacy-preserving classification, and
present an efficient classifier which is private in the
differential privacy model of Dwork et al. Our classifier works in the
ERM (empirical loss minimization) framework, and includes privacy
preserving logistic regression and privacy preserving support
vector machines. We show that our classifier is private, provide
analytical bounds on the sample requirement of our classifier,
and evaluate it on real data. We next address the question of differentially
private statistical estimation. We draw a concrete connection between
differential privacy, and gross error sensitivity, a measure of robustness
of a statistical estimator, and show how these two notions are
Based on joint work with Claire Monteleoni (George Washington University),
Anand Sarwate (Rutgers University), and Daniel Hsu (Columbia University).
Kamalika Chaudhuri is an Assistant Professor in Computer Science and
Engineering at UC San Diego. She received her PhD from the University of
California at Berkeley in 2007, and was a postdoctoral researcher at UC San
Diego from 2007-2010. Her research is
on the theoretical foundations of machine learning, and she is interested
in a variety of topics including unsupervised learning, confidence in
prediction, and privacy-preserving machine learning. She is the recipient
of an NSF CAREER Award in 2013.
Computer Science, Boston University
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