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

Program committee

Program committee chairs (in alphabetical order of last name)

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)

  1. Ulf Brefeld, Yahoo Research, Catalonia, Spain
  2. Michael Bruckner, University of Postdam, Germany
  3. Mike Burmester, Florida State University, FL, USA
  4. Kamalika Chaudhuri, University of California at San Diego, USA
  5. Peter Christen, Australian National University, Australia
  6. Chris Clifton, Purdue University, USA
  7. Maria Luisa Damiani, University of Milano, Italy
  8. Juan M. Estevez-Tapiador, University of York, UK
  9. Elena Ferrari, University of Insubria, Italy
  10. Dimitrios Kalles, Hellenic Open University, Greece.
  11. Murat Kantarcioglu, University of Texas at Dallas, USA
  12. Kun Liu, Yahoo! Labs, California, USA
  13. Daniel Lowd, University of Oregon, USA
  14. Grigorios Loukides, Vanderbilt University, USA
  15. Emmanuel Magkos, Ionian University, Greece
  16. Bradley Malin, Vanderbilt University, USA
  17. Mohamed Mokbel, University of Minnesota, 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. Aaron Roth, Carnegie-Mellon University, USA
  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. Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
  28. Shobha Venkataraman, AT&T, USA
  29. Philip S. Yu, University of Illinois at Chicago, USA


The accepted papers are available online.

Post-workshop proceedings of revised versions of the papers will be published by Springer in the Lecture Notes in Aritificial Intelligence LNCS series.

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.

Authors of selected papers related to learning, games and security (themes 2 and 4) will be invited to prepare a substantially revised and extended version of their work for publication to the journal of IEEE transactions on dependable and secure computing.