Mar. 3, 2003
4 - 5 PM
2310 CS
|
Postponed!
TBA
Ian Alderman
(web)
University of Wisconsin, Madison
Department of Computer Sciences
(web)
Privacy-Preserving Data Mining
URL: http://www.almaden.ibm.com/cs/quest/papers/sigmod00_privacy.pdf
This is a presentation of a paper by
R. Agrawal and R. Srikant, from the
2000 SIGMOD conference.
A fruitful direction for future data
mining research will be the
development of techniques that
incorporate privacy
concerns. Specifically, we address the
following question. Since the primary
task in data mining is the development
of models about aggregated data, can
we develop accurate models without
access to precise information in
individual data records? We consider
the concrete case of building a
decision-tree classifier from training
data in which the values of individual
records have been perturbed. The
resulting data records look very
different from the original records
and the distribution of data values is
also very different from the original
distribution. While it is not possible
to accurately estimate original values
in individual data records, we propose
a novel reconstruction procedure to
accurately estimate the distribution
of original data values. By using
these reconstructed distributions, we
are able to build classifiers whose
accuracy is comparable to the accuracy
of classifiers built with the original
data.
|
Mar. 24, 2003
4 - 5 PM
2310 CS
|
Postponed!
New date, time, and location:
Friday, April 4 -- 2:00 PM -- 1221 CS
Anuj Desai (web)
University of Wisconsin, Madison
Law School
web
Topic: DMCA
Cookies: at the talk
|