“Open Algorithm (OPAL) for Public Data and Good” seeks to merge different “privacy enhancing techniques” (PETs), such as federated learning, differential privacy, and negative databases, to allow trusted third parties such as researchers or official institutions to analyze censuses or national surveys’ microdata produced by national statistical offices (NSOs), as well as other administrative records, to derive indicators using these data, while avoiding privacy risks. A pilot is expected to be conducted in Mexico, and DPA plans to expand to additional NSOs and other public data holders in the future.