Madison

Date/Time: April 17 2007 11:00 a.m.

Coffee: 10:45
a.m.

Location: ACES 2.302

Host: Don Batory

Talk Tit

le: Anonymization Techniques for Published Data

Talk Abstract:
M

any organizations publish and distribute non-aggregate
personal data fo

r purposes including medical demographic
and public health research.
For legal and ethical reasons
it is important that these organization

s take steps to protect
the identities of individuals as well as their
sensitive
personal information. At the same time concern for privacy

must be balanced with the need to provide useful high-quality data.

In this talk I will first give a brief overview of the
anonymity

problem in data publishing. Then I will describe
a new multidimensiona

l generalization approach (also commonly
called recoding) and greedy al

gorithmic framework.

The contributions of this work span two key dim

ensions:
First there are a seemingly infinite number of ways to
me

asure data quality. I will take a very direct evaluation
approach bas

ed on a target workload of queries and data
mining tasks and I will de

scribe some ways to directly
incorporate knowledge of a workload into t

he anonymization
process. Second as more and more personal informatio

n
is collected it is important to develop algorithms that
are both
efficient and scalable. In the latter part of
the talk I will descri

be techniques for incorporating scalability
into our algorithmic framew