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IEEE PADM 2011 : 3rd IEEE Intl. Workshop on Privacy Aspects of Data Mining (PADM 2011)

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Link: http://www.zurich.ibm.com/padm2011/
 
When Dec 10, 2011 - Dec 10, 2011
Where Vancouver, Canada
Submission Deadline Aug 5, 2011
Notification Due Sep 20, 2011
Final Version Due Oct 11, 2011
Categories    privacy   data mining   machine learning
 

Call For Papers

Note the new Submission Deadline: August 5, 2011

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3rd IEEE Intl. Workshop on Privacy Aspects of Data Mining (PADM 2011)
[ please distribute - apologies for multiple postings ]
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Call for Papers --- PADM 2011
3rd IEEE International Workshop on Privacy Aspects of Data Mining
(in conjunction with ICDM 2011, December 10, 2011, Vancouver, Canada)
http://www.zurich.ibm.com/padm2011/
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IMPORTANT DATES
* Workshop paper submission deadline: ** Aug 5, 2011 (11:59pm Hawaii time) **
* Workshop paper acceptance notification: September 20, 2011
* Workshop paper camera-ready deadline: October 11, 2011
* Workshop meeting: December 10, 2011

OVERVIEW
Machine learning and data mining algorithms have now penetrated our everyday lives in a wide range of applications, like web search, social networking, communication networks, mobility data, advertising, cloud computing, and business intelligence. Each application can have its own privacy requirements, which include protection of personal information and statistical disclosure control, protection of business secrets, and knowledge hiding in general. Adaptations of traditional privacy definitions such as randomized response, k-anonymity, and differential privacy to these new applications do not always adequately protect sensitive information. Attacks on supposedly anonymized data and additional privacy issues raised in the literature have shown that developing a robust privacy definition for a new application requires a tremendous feat of engineering. Another challenge in privacy preserving data analysis is maintaining accuracy (or utility) of the algorithms. There is an inherent trade-off between privacy and utility which needs to be formally captured for each data mining task. Often, the utility guarantee provided by an algorithm is dependent on the privacy notion it satisfies. Comparing the utilities of algorithms that implement different privacy definitions is still an open challenge. Where theoretical utility analysis is difficult or impossible, it is important to develop performance and data benchmarks, facilitating reliable comparison of competing methods. Finally, there is a need to address the new privacy challenges presented by emerging applications such as mobility data mining, social network analysis, advertising, and cloud computing. PADM will be a full-day workshop that will be held in conjunction with the IEEE ICDM 2011 conference in Vancouver, Canada. The purpose of this workshop is to encourage principled research that develops methodologies to address open privacy problems.

TOPICS OF INTEREST
The topics of interest include, but are not limited to, the following areas:
* Disclosure prevention of sensitive information when the attacker has detailed knowledge about the privacy mechanisms
* Design guidelines for privacy definitions
* Measuring/comparing utility across different privacy definitions
* Techniques for analyzing perturbed data (e.g. uncertain data analysis)
* Privacy under simultaneous, independent leakages or prior release of information (composition)
* Statistical disclosure control and privacy in social and physical sciences
* Information-theoretic or computational barriers to privacy and utility
* Privacy preservation against data manipulation prior to anonymization
* Privacy preservation using knowledge hiding
* Privacy challenges in emerging applications such as mobility, cloud computing, advertising, and social networking
* Privacy challenges in location-based social networks (e.g. Foursquare, Gowalla)
* Benchmarks and data sets for testing privacy preserving algorithms in emerging application areas

SUBMISSION GUIDELINES
Paper submissions should be limited to a maximum of 8 pages in the IEEE 2-column format. All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to privacy aspects of data mining, originality, significance, and clarity. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered for PADM 2011. The authors of a small number of selected (best) papers from the workshop will be invited to prepare a substantially revised and extended version of their work for publication to a special issue that will be organized after the workshop.

WORKSHOP ORGANIZERS
* Raghav Bhaskar, Microsoft Research, India
* Aris Gkoulalas-Divanis, IBM Research-Zurich, Switzerland
* Dan Kifer, Pennsylvania State University, USA
* Srivatsan Laxman, Microsoft Research, India

PROGRAM COMMITTEE
* Chris Clifton, Purdue University, USA
* Josep Domingo-Ferrer, Universitat Rovira i Virgili, Catalonia
* Michael Hay, Cornell University, USA
* Panos Kalnis, King Abdullah University of Science and Technology, Saudi Arabia
* Hillol Kargupta, University of Maryland Baltimore County, USA
* Kun Liu, Yahoo! Labs, California, USA
* Grigorios Loukides, Vanderbilt University, USA
* Ashwin Machanavajjhala, Yahoo! Research, USA
* Frank McSherry, Microsoft Research, USA
* Gerome Miklau, University of Massachusetts, Amherst, USA
* Mohamed Mokbel, University of Minnesota, USA
* Mehmet Sayal, Hewlett Packard, USA
* Yucel Saygin, Sabanci University, Turkey
* Aleksandra Slavkovic, Penn State University, USA
* Adam Smith, Penn State University, USA
* Trian Marius Truta, Northern Kentucky University, USA
* Philip S. Yu, University of Illinois at Chicago, USA
* Jaideep Vaidya, Rutgers University, USA
* Li Xiong, Emory University, USA

More information about PADM 2011 can be found at: http://www.zurich.ibm.com/padm2011/
Questions should be directed to the workshop co-chairs at: padm2011.workshop (at) gmail.com

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