ECML/PKDD Workshop on Privacy and Security issues in Data Mining and
Machine Learning (PSDML 2010)

Barcelona, Spain, 24 September 2010

*****Call for papers*****

Privacy and security-related aspects of data mining and machine
learning have been the topic of active research during the last few
years, due to the existence of numerous applications with privacy
and/or security requirements.  Privacy issues have become a serious
concern due to the collection, analysis and sharing of personal data
by privately owned companies and public sector organizations for
various purposes, such as data publishing or data mining.  This has
led to the development of privacy-preserving data mining and machine
learning methods. More general security considerations arise in
applications such as biometric authentication, intrusion detection and
response, and malware classification. This has led to the development
of adversarial learning algorithms, while parallel work in multi-agent
settings and in low regret learning algorithms has revealed
interesting interplays between learning and game theory.

Although significant research has so far been conducted, numerous
theoretical and practical challenges remain. Firstly, several emerging
research areas in data analysis (such as stream mining, mobility data
mining, social network analysis), decision making and machine learning
(such as fraud detection, intrusion detection and response), require
new theoretical and applied techniques for the offering of privacy or
security. Secondly, there is an urgent need for learning and mining
methods with sufficient privacy and security guarantees for critical
applications (i.e. biomedical, financial, mobility). Thirdly, there is
an emerging demand for security applications such as biometric
authentication, malware detection and spam filtering. Finally, large
scale systems require data integration and linkage, information
sharing and decision making in a secure and privacy-preserving manner
over a wide network. Further research is required to provide scalable
methodologies on very large datasets, with a large number of parties,
for privacy and security applications. In all cases, the strong
interconnections between data mining and machine learning,
cryptography and game theory, create the need for the development of
multidisciplinary approaches on adversarial learning and mining

***** Aims and scope *****

The aim of this workshop is to bring together scientists and
practitioners who conduct cutting edge research on privacy and
security issues in data mining and machine learning to discuss the
most recent advances in these research areas, identify open problem
domains and research directions, and propose possible solutions. We
invite interdisciplinary research on cryptography, data mining, game
theory, machine learning, privacy, security and statistics. Moreover,
we invite mature contributions as well as interesting preliminary
results and descriptions of open problems on emerging research domains
and applications of privacy and security in data mining and machine

***** Core themes and topics of interest *****

The workshop invites original submissions in any of the following core
subjects. For each subject we provide an indicative list of topics of
   1. Data privacy and security issues.
         1. Privacy-preserving data publishing and anonymity.
         2. Privacy-aware data fusion, integration and record linkage.
         3. Privacy evaluation techniques and metrics.
         4. Auditing and query execution over private data.
         5. Privacy-aware access control.
   2. Theoretical aspects of machine learning for security applications.
         1. Adversarial classification, learning and hypothesis testing.
         2. Learning in unknown and/or partially observable stochastic games.

         3. Special learning problems in security applications (i.e.
           learning with distribution shifts, semi-supervised learning, learning
           in large datasets).
         4. Distributed inference and decision making for security.
         5. Game-theoretic topics related to security applications.
   3. Privacy-preserving data mining, machine learning and applications.
         1. Emerging research domains in privacy-preserving mining and learning
            (e.g., stream mining, social network analysis, graph analysis).
         2. Application-specific privacy preserving data mining and machine
         3. Knowledge hiding approaches for privacy preserving learning and
         4. Secure multiparty computation and cryptographic approaches.
         5. Statistical approaches for privacy preserving data mining.
   4. Security applications of machine learning.
         1. Cryptographic applications of machine learning.
         2. Intrusion detection and response.
         3. Biometric authentication, fraud detection.
         4. Statistical analysis and classification of malware.
         5. Spam filtering and captchas.

***** Important dates *****

    * Workshop paper submission deadline: 28 June, 2010
    * Workshop paper acceptance notification: 18 July, 2010
    * Workshop paper camera-ready deadline: 30 July, 2010
    * Workshop: 24 September, 2010

***** Organizing committee *****

    * Christos Dimitrakakis, Goethe University of Frankfurt, Germany
    * Aris Gkoulalas-Divanis, IBM Research Zurich, Switzerland
    * Aikaterini Mitrokotsa, EPFL University, Switzerland
    * Yucel Saygin, Sabanci University, Turkey
    * Vassilios S. Verykios, University of Thessaly, Greece

Christos Dimitrakakis and Aikaterini Mitrokotsa are chairs for the areas of
machine learning and security applications. Aris Gkoulalas-Divanis, Yucel
Saygin and Vassilios S. Verykios are area chairs for privacy and privacy
preserving data mining.

**** Program committee members (in alphabetical order of last name;
tentative list) ****

   1. Luca de Alfaro, UCSC, USA
   2. Ulf Brefeld, Yahoo Research, Catalonia, Spain
   3. Michael Bruckner, University of Postdam, Germany
   4. Mike Burmester, Florida State University, FL, USA
   5. Peter Christen, Australian National University, Australia
   6. Chris Clifton, Purdue University, USA
   7. Maria Luisa Damiani, University of Milano, Italy
   8. Christos Douligeris, University of Piraeus, Greece
   9. Elena Ferrari, University of Insubria, Italy
  10. Julio-Cesar Hernandez-Castro, University of Portsmouth, UK
  11. Kun Liu, Yahoo! Labs, California, USA
  12. Daniel Lowd, University of Oregon, USA
  13. Grigorios Loukides, Vanderbilt University, USA
  14. Emmanuel Magkos, Ionian University, Greece
  15. Bradley Malin, Vanderbilt University, USA
  16. Mohamed Mokbel, University of Minnesota, USA
  17. Murat KantarcΡ±oglu, University of Texas at Dallas, USA
  18. Blaine Nelson, UC Berkeley, USA
  19. Ercan Nergiz, Sabanci University, Turkey
  20. Roberto Perdisci, Georgia Institute of Technology, USA
  21. Pedro Peris-Lopez, TU Delft, Netherlands
  22. Norman Poh, University of Surrey, UK
  23. Benjamin I. P. Rubinstein, University of California, USA
  24. Jianhua Shao, Cardiff University, UK
  25. Jessica Staddon, PARC, USA
  26. Angelos Stavrou, George Mason University, USA
  27. Juan M. Tapiador, University of York, UK
  28. Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
  29. Shobha Venkataraman, AT&T, USA
  30. Philip S. Yu, University of Illinois at Chicago, USA

***** Paper requirements and submission guidelines *****

In order to ensure that all papers fit within the workshop's theme, we
require that all submissions either a) directly involve both a privacy
or security issue and a machine learning or data mining topic or b)
address a fundamental issue in data mining or machine learning that
has clear privacy or security implications.

All papers must be submitted electronically, in PDF or PS
format. Submitted papers must be written in English and have no
significant overlap with published papers or submissions to other
journals, conferences or workshops.  Submissions must be at most 14
pages (single column) long and formatted according to Springer-Verlag
LNCS guidelines. We also invite short (4-6 pages) position papers,
describing open problems or on-going research. These papers will
undergo the same review process and selected papers will be presented
at the final session of the workshop.

The workshop proceedings will be published by Springer-Verlag in the LNCS
series. All accepted papers will in addition be published at the workshop's

The authors of the three best papers from the workshop that are
related to privacy (core themes 1 and 3) will be invited to prepare a
substantially revised and extended version of their work for
publication to the journal of Transactions on Data Privacy.
Arrangements for a special issue for papers from themes 2 and 4 are