The collection, analysis and sharing of person-specific data for various purposes, like publication or data mining, raise serious concerns about the privacy of individuals who are represented in the data, as well as the sensitive knowledge patterns that can be exposed when mining the data by the existing data mining technology. To address these concerns, the domain of privacy-preserving data mining was brought into existence a decade ago. Since then, a wide variety of methodologies for privacy-aware data sharing and integration, privacy-preserving data publication and privacy-preserving data mining, have been developed. Although significant research on this domain has been conducted over the last years, there are still numerous challenges that require further investigation both from a theoretical and from a practical point of view. First of all, emerging research areas such as stream mining, mobility data mining and social network analysis, require new theoretical and applied techniques for the offering of privacy. Second, there is an urging need for privacy methodologies that can offer guarantees about the level of achieved data quality and utility, and thus be suitable for a variety of data demanding applications, such as biomedical and healthcare studies, location-based services and e-commerce. Third, the integration or linkage of data in a privacy preserving manner along with the privacy-aware collaborative mining of data require further stimulus to provide scalable methodologies on very large datasets and large number of parties, while offering a high level of privacy.

PADM will be a full-day workshop that will be held in conjunction with the IEEE ICDM 2010 conference in Sydney, Australia. Following last year’s event, PADM 2010 will seek submissions that cover state-of-the-art research on privacy and security aspects of data mining, with particular focus on the applications of privacy and security in emerging domains.