With the ever-increasing amount of data collected everyday, data confidentiality is more and more at risk. Many of the traditional approaches to statistical disclosure control are no longer sufficient to protect the confidentiality of the data. Formal privacy guarantees - provable privacy guarantees that hold regardless of assumed knowledge and attack strategy of a malicious user - are becoming increasingly important for large producers of statistics, such as national statistical agencies or large private companies. These organizations need to design and engineer systems with effective formally private disclosure limitation systems. This session brings together experts who have developed, proposed, and implemented formal privacy models such as variants of differential privacy in various large organizations. The presentations will inform attendees of challenges that were identified, how they were met, and the outlook for future implementations.