Privacy concerns have been a major hurdle for the publication of sensitive data, e.g., biomedical records and web logs, as well as the analytical results derived from such data. This motivates techniques for privacy-preserving data publication and analysis, as they can be key enablers and accelerators of the research that relies on sensitive data. In the last decade, a variety of privacy protection schemes have been studied in the data management community, ranging from early proposals such as k-anonymity and its variants, to the recently proposed, much stronger differential privacy model. Meanwhile, with the advances on privacy protection schemes, it is highly demanding to design algorithms and systems for data publication and analysis that satisfy their privacy guarantees. The PrivDB workshop will provide a forum for data privacy researchers to exchange new results on privacy-preservation problems. PrivDB’13 solicits research papers, work-in-progress reports, system demonstrations, and experimental studies from academia and industry.