Robustness has been studied a lot in statistics since the seminal work of Huber. Many algorithms have also been designed in Machine Learning. However, this subject has witnessed an important renew during the last 10 years both in the statistical and computer science communities. It involves statistics, optimization, probability and machine learning as mathematical domains. In the meantime, privacy has received a lot of attention in the Computer science community because it is a central issue for the security of many sensitive data in finance, economics, administration and, more basically, for keeping customers’ trust in trading. It seems however possible to randomize data in order to ensure a controlled amount of privacy of the individuals while still being able to learn some patterns out of it: this is the cornerstone of privacy. Only very recently, statisticians started to analyze privacy mechanisms. In particular, they discovered several interesting common features between robustness issues and privacy. That is the main motivation on our side to organize a joint conference simultaneously on the two subjects. We believe that interesting interaction between people working on robustness and privacy may result in interesting collaborations. These fields have become mature enough in order to organize a three day workshop on this subject.