Abstract: Embodiments disclosed herein improve data privacy and security by combining synthetic data and statutory pseudonymization to create protected data that is more effectively disconnected from the original source data. By bringing synthetic data and statutory pseudonymization techniques together, a flexible level of protection may be applied to data that strikes an appropriate balance between the ease of use of cleartext data and the aggressive protection of statutory pseudonymization. Further embodiments disclosed herein improve data privacy and security by providing a novel statistical framework that jointly quantifies different types of privacy risks in synthetic datasets and that includes attack-based evaluations for the singling out, linkability, and inference risks. According to other embodiments, the modular nature of the framework facilitates the future integration of new and potentially stronger attacks for evaluating privacy risks.