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International Workshop on Privacy-Preserving Data Analysis (PPDA)
April 20, 2009, Brisbane, Australia
http://www.cse.ust.hk/~raywong/ppda

to be held in conjunction with DASFAA 2009
(http://www.itee.uq.edu.au/~dasfaa/)

CALL FOR PAPERS

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With the advancement of information technology and declining hardware
price, organizations and companies are able to collect large amount of
personal data. Moreover, advanced data analysis and mining techniques
have been proposed to derived patterns hidden in data. However, the
increasing power in data processing and analysis also raises concerns
over the proper usage of personal information. It was found that a
sensitive medical record was uniquely linked to a named voter record
in a publicly available voter list through the shared attributes of
Zip, Birth date and Sex. It is not surprising that many organizations
are reluctant to disclose their data even when there may be great
potential gains from the data exploration. Therefore research in the
area of privacy preservation is deemed important for both theoretical
and practical reasons. PPDA 2009 will be held in conjunction with
DASFAA 2009 conference, and aims to bring together researchers in
different fields related to privacy preserving data analysis and to
provide a forum where researchers and practitioners can share and
exchange their knowledge and experience.

PPDA 2009 will be published in a combined volume of Lecturer Notes
in Computer Science series published by Springer (pending for
approval). We invite original research contributions on generic and
applied research on privacy. The PPDA 2009 topics of interest include
but are certainly not limited to the following areas:

- Privacy Preserving Data Publishing
- Background Knowledge Formulation
- Privacy-Aware Access Control
- Privacy in Outsourced Databases
- Privacy in Spatial Databases
- Cryptographic Tools
- Privacy Preservation over Social Networks
- Privacy Preservation over Graphs
- Sequential Releases
- Privacy over Data Streams
- Protocol-based Privacy Preserving Data Mining
- Case Studies on Privacy Leakage on Real Applications
- Efficient Algorithm for Privacy Preservation
- Generalization-based Algorithm
- Perturbation-based Algorithm
- Statistical Databases
- Privacy-preserving Data Integration
- Privacy Evaluation and Metrics
- Privacy Models
- Privacy in Web environment and Business Environment
- Learning for Privacy Breach
- Threats to Privacy
- Watermarking
- Security and Semantic Web
- Data Hiding

Important Dates:
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Full paper submission: October 10, 2008
Acceptance Notification: December 12, 2008
Camera-ready copy: December 19, 2008

Submission:
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Authors should submit papers reporting original works that are
currently not under review or published elsewhere. The paper should
be submitted in PDF format, with maximum length fifteen (15) pages,
following Springer-Verlag's LNCS manuscript submission guidelines,
available at http://www.springer.de/comp/lncs/authors.html. The
submission details will be announced soon.

Program Chairs:
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Raymond Chi-Wing Wong,
The Hong Kong University of Science and Technology

Ada Wai-Chee Fu,
The Chinese University of Hong Kong


PC:
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Josep Domingo-Ferrer, Rovira i Virgili University
Benjamin C. M. Fung, Concordia University
Christopher Andrew Leckie, The University of Melbourne
Jiuyong Li, University of South Australia
Jun-Lin Lin, Yuan Ze University
Kun Liu, IBM Almaden Research Center
Bradley Malin, Vanderbilt University
Wee Keong Ng, Nanyang Technological University
Yufei Tao, the Chinese University of Hong Kong
Vicenc Torra, Universitat Autonoma de Barcelona
Carmela Troncoso, K.U.Leuven
Ke Wang, Simon Fraser University
Sean Wang, University of Vermont
Duminda Wijesekera, George Mason University
Xintao Wu, University of North Carolina at Charlotte
Jeffrey Yu, the Chinese University of Hong Kong
Philip S. Yu, University of Illinois at Chicago