|
|
Curriculum Vitae
(also in postscript, and pdf).
See referenced publications for more details,
or C. V. for full publication list.
Data is ubiquitous, but knowledge is scarce. Data mining - finding
List of resources
in this field courtesy
Stanley Oliveira.
Stanford PORTIA project
reading list.
Privacy issues in distributed data mining is only one area where
data mining and security interact.
Other areas of research include security concerns posed by data mining
results (the data isn't private, but what might be learned from
it is) and applications of data mining to security (e.g., intrusion detection).
Spatio-Temporal Data Mining raises a whole new set of problems.
Spatial data is complex in character, and requires new techniques for
doing data mining.
None funded at this time. If you would like to pursue a independent study with me, please see
the instructions on proposing
such study. Occasionally I have specific projects that may be of
interest, often involving collaboration with corporate partners.
These are listed below:
Jaideep S. Vaidya
Associate Professor-Computer Information Systems
Rutgers, The State University of New Jersey
Management Science and Information Systems Department
1 Washington Park
Newark, New Jersey, 07102-1897
Office: WP 1080
Office Phone: +1 973-353-1441
FAX: +1 973-353-5003
jsvaidya@rbs.rutgers.edu
New: This year, I am organizing the Workshop on Privacy in the Electronic
Society (WPES '11) held in conjunction with ACM CCS. Please
consider sending your papers covering any aspect of privacy to the
workshop.
Notes to students pursuing a
Ph.D.,
Master's,
Independent Study,
or desiring
admission to Rutgers.
Current Areas of Research
Privacy-Preserving Data Mining
interesting
patterns from data, relies on the collection of
massive amounts of data, often from multiple different sites. However,
privacy/security issues often restrict access to data. Is it possible
to mine data when access to it is restricted? My goal is to develop
technology to address this in the distributed case: such that only
data owners or their representatives have true access to data, but
accurate mining results are still computed. We develop algorithms that
share some information to calculate correct results, where we can show
that the shared information does not disclose private data.
Selected Presentations and Publications:
Privacy Preserving K-Means Clustering on Vertically Partitioned
Data
,
invited talk at Interface
'04, the best of data mining at KDD session, Baltimore, Maryland,
May 27, 2004.
Privacy Preserving Data
Mining over
Vertically Partitioned Data
at the CSIS
Seminar at the Department of
Information
and Software Engineering, George
Mason
University, May 25, 2004.
Privacy Preserving Data
Mining over
Vertically Partitioned Data
at the DAIS
Seminar at the CS Department at
University
of Illinois at Urbana-Champaign, October 24, 2003.
Secure Set Intersection
Cardinality with Application to Association Rule Mining
.
Jaideep Vaidya and Chris Clifton,
accepted for publication in
Journal of Computer
Security,
IOS Press.
Privacy-Preserving Data Mining: Why, How, and What For?
.
Jaideep Vaidya and Chris Clifton,
accepted for publication in
IEEE Security &
Privacy,
New York, NY.
Privacy Preserving Outlier Detection
, Jaideep Vaidya and Chris
Clifton, in the 2004 IEEE International Conference on Data Mining,
November 2004, Brighton, UK.
Privacy-Preserving
K-Means
Clustering over Vertically Partitioned Data
.
Jaideep Vaidya and Chris Clifton,
The Ninth ACM
SIGKDD
International Conference on Knowledge Discovery and Data
Mining,
August 24 - 27, 2003,
Washington, D.C.
Honorable mention (runner up), best research paper.
(Revised paper invited for submission at Interface '04).
Collaborators
Other Data Mining and Security Topics
Spatio-Temporal Data Mining
Students and Collaborators
Postdoctoral Opportunities
Potential Independent Study Projects