The first Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010) was organized on September 24, 2010 at Barcelona, Spain, in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Privacy and security-related aspects of data mining and machine learning have been the topic of active research during the last decade due to the existence of numerous applications with privacy and/or security requirements. Privacy issues have become a serious concern due to the increasing collection and sharing of personal d ata for purposes like 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 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, we are still far from addressing the numerous theoretical and practical challenges that arise in this domain. Firstly, several emerging research areas in data analysis, decision making and machine learning, 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 privacy and security guarantees. Thirdly, there is an emerging demand for security applications such as biometric authentication and malware detection. 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.