54.txt

This complete matlab for neural network

发信人: NAOMIELIE (雁来红), 信区: DataMining
标  题: [CFP] 2nd Workshop on Privacy Preserving DM
发信站: 南京大学小百合站 (Wed Jul  9 12:22:52 2003)


2nd Workshop on Privacy Preserving Data Mining (PPDM)
Melbourne, Florida, USA, November 19, 2003

In conjunction with
ICDM'03: The Third IEEE International Conference on Data Mining 2003

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Abbreviated Call for Papers
(Full CFP at http://www.cis.syr.edu/~wedu/ppdm2003/cfp.html )

In the light of developments in technology to analyze personal data,
public concerns regarding privacy are rising.  While some believe that
statistical and Knowledge Discovery and Data Mining (KDDM) research is
detached from this issue, we can certainly see that the debate is
gaining momentum as KDDM and statistical tools are more widely adopted
by public and private organizations hosting large databases of
personal records.  One of the key requirements of a data mining
project is access to the relevant data.  Privacy and Security concerns
can constrain such access, threatening to derail data mining projects.
This workshop will bring together researchers and practitioners to
identify problems and solutions where data mining interferes with
privacy and security.

The purpose of this workshop is to discuss these issues and promote
achievements of researchers in the area.  We want to bring together
experts, including both researchers and practitioners, in privacy,
data mining and its applications, and statistical database security.

Accepted papers will be published in the ICDM workshop proceedings,
including placement in the IEEE digital library.

Topics of Interest

Papers are solicited that identify and propose technical solutions to
such problems.  Sample topics (by no means an exhaustive list) include:

    * Meanings and measuring of ``privacy'' in privacy-preserving data
      mining.
    * Learning from perturbed/obscured data.
    * Techniques for protecting confidentiality of sensitive
      information, including work on statistical databases, and
      obscuring or restricting data access to prevent violation of
      privacy and security policies.
    * Learning from distributed data sets with limits on sharing of
      information.
    * Hiding knowledge in data sets.
    * Underlying methods and techniques to support data mining while
      respecting privacy and security (e.g., secure multi-party
      computation).
    * The relationship between privacy and knowledge discovery, and
      algorithms for balancing privacy and knowledge discovery.
    * Use of data mining results to reconstruct private information, and
      corporate security in the face of analysis by KDDM and statistical
      tools of public data by competitors.
    * Use of anonymity techniques to protect privacy in data mining.

What and how to submit

Papers should be at most 12 pages long in single-column format, 12-point
font, with at least 1-inch margins on all sides.  Please send them
electronically (PDF or PostScript files) to [e-mail] on or
before August 29, 2003.

Important Dates

Intent to submit
(appreciated, not required)	August 22, 2003
Paper submission	August 29, 2003
Notification of acceptance	September 26, 2003
Camera ready papers	October 10, 2003
Workshop date	November 19, 2003

Organizers

* Wenliang (Kevin) Du (Chair),
  Syracuse University
  Department of Electrical Engineering and Computer Science
  Syracuse, NY 13244 USA
  [phone], Fax: [phone]
  [e-mail]
  http://www.cis.syr.edu/~wedu/

* Chris Clifton, Purdue University (Co-Chair)
  http://www.cs.purdue.edu/people/clifton

Program Committee

* Wesley Chu, University of California, Los Angeles
* Vladimir Estivill-Castro, Griffith University
* Johannes Gehrke, Cornell University
* Tom Johnsten, University of South Alabama
* Hillol Kargupta, University of Maryland Baltimore County
* Stanley R. M. Oliveira, Embrapa Information Technology
* Benny Pinkas, Trusted Systems Lab, HP Labs
* Vijay V. Raghavan, University of Louisiana Lafayette
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