ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010) Barcelona, Spain, September 24, 2010. Submission deadline: 28 June 2010. http://fias.uni-frankfurt.de/~dimitrakakis/workshops/psdml-2010/ 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. A. 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. B. 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. C. 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. D. 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.