Lomas is an open-source platform developed by the Federal Statistical Office that resolves the tension between data privacy and transparency. It allows researchers and analysts to run algorithms on confidential public data without accessing raw information, using Differential Privacy to guarantee protection while maintaining full transparency. By keeping this solution open-source and publicly controlled, Lomas strengthens digital sovereignty and offers a concrete alternative to proprietary "black box" systems, demonstrating that democratic societies can have both privacy and transparency.