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CSLI Calendar, Wednesday, 11 August 2004, vol. 19:48
CSLI CALENDAR OF PUBLIC EVENTS
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11 August 2004 Stanford Vol. 19, No. 48
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A weekly publication of the
Center for the Study of Language and Information (CSLI)
Stanford University, Cordura Hall, Stanford, CA 94305-4115
http://www-csli.stanford.edu/
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ACTIVITIES FROM 11 AUGUST 2004 TO 20 AUGUST 2004
THURSDAY, 12 AUGUST 2004
4:00pm PARC Forum
George Pake Auditorium at PARC
"The Role of Solar Power in Rural Development"
Charles Gay
Greenstar
http://www.parc.com/forum/
FRIDAY, 13 AUGUST 2004
3:00pm Stanford Security Seminar
Gates 459
"Privacy-Preserving Bayesian Network Structure Computation on
Distributed Heterogeneous Data"
Rebecca Wright
Stevens Institute of Technology
http://theory.stanford.edu/seclab/sem.html
Abstract below
MONDAY, 16 AUGUST 2004
4:15pm fMRI Colloquium
Clark Center Auditorium
"Music and the Brain: Auditory Plasticity and Sparse Temporal Sampling"
Nadine Gaab
Psychology
http://www-med.stanford.edu/sbrc/calendar/
WEDNESDAY, 18 AUGUST 2004
4:15pm CSLI Seminar on Computational Learning and Adaptation
Cordura Hall, room 100
"The bias-variance tradeoff in active learning"
Hinrich Schuetze
Enkata Technologies
http://cll.stanford.edu/scla.html
THURSDAY, 19 AUGUST 2004
4:00pm PARC Forum
George Pake Auditorium at PARC
"The Dish"
Michael Cousins
SRI International
http://www.parc.com/forum/
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Stanford Blood Center status: Shortage of O+, O-, and A-.
For an appointment: http://bloodcenter.stanford.edu/ or call
650-723-7831. It only takes an hour of your time.
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STANFORD SECURITY SEMINAR
on Friday, 13 August 2004, 3:00pm
Gates 459
http://theory.stanford.edu/seclab/sem.html
"Privacy-Preserving Bayesian Network Structure Computation
on Distributed Heterogeneous Data"
Rebecca Wright
Stevens Institute of Technology
http://www.cs.stevens-tech.edu/~rwright/
As more and more activities are carried out using computers and
computer networks, the amount of potentially sensitive data stored by
business, governments, and other parties increases. Different parties
may wish to benefit from cooperative use of their data, but privacy
regulations and other privacy concerns may prevent the parties from
sharing their data. Privacy-preserving data mining provides a solution
by creating distributed data mining algorithms in which the underlying
data is not revealed. We present a privacy-preserving protocol for a
particular data mining task: learning the Bayesian network structure
for distributed heterogeneous data. In this setting, two parties
owning confidential databases wish to learn the structure of a
Bayesian network on the combination of their databases without
revealing anything about their data to each other. We give an
efficient and privacy-preserving version of the K2 algorithm to
construct the structure of a Bayesian network for the parties' joint
data. (Joint work with Zhiqiang Yang.)
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