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
problems.

***** 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 learning.

***** 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 interest.
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
learning.
3. Knowledge hiding approaches for privacy preserving learning and
mining.
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 Kantarc?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
webpage.

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
underway.