The master class aims to provide an overview of statistical disclosure control from the perspective of the statistical agency up until now.  I focus on traditional forms of    statistical data:  microdata from social surveys and tabular data from censuses, surveys and registers,  and discuss  the types of disclosure risks, statistical disclosure control  (SDC) methods and  the quantification of disclosure risk and data utility. However, these traditional forms of statistical data and their confidentiality protection rely heavily on assumptions that may no longer be relevant. In recent years, we have seen the digitalization of all aspects of our society leading to new and linked data sources offering unprecedented opportunities for research and evidence-based policies. These developments have put pressure on statistical agencies to provide broader access to their data. On the other hand, with detailed personal information easily accessible from the internet, traditional SDC methods may no longer be sufficient and this has led to the opposite effect of statistical agencies restricting and licensing data as an SDC method.  To meet the demands and challenges for disseminating more open and accessible data through for example  web-based platforms where outputs are generated and protected on the-fly without the need for human intervention, statistical agencies have been investigating more rigorous data protection mechanisms  to incorporate into their SDC tool-kit. One such mechanism is Differential Privacy, a mathematically principled method of measuring how secure a protection mechanism is with respect to personal data disclosures. In this master class, we present some future dissemination strategies under considered by statistical agencies and the potential for Differential Privacy to protect the confidentiality of data subjects with well-defined privacy guarantees.