This project uses Listeria monocytogenes contamination in food processing facilities as a model to develop new digital-twin models augmented with privacy-guaranteed machine learning solutions for food safety assessment. The key challenges in pathogen environmental monitoring programs stem from the high cost in testing and experimentation, the high risk in contamination and outbreak, and the reluctance of individual facilities to share data due to privacy and liability concerns. This research tackles these challenges through a three-pronged approach consisting of (i) digital-twin models for data generation and environmental monitoring, (ii) graph analytics and reinforcement learning for pathogen detection, and (iii) privatization techniques for data protection. This proposed integrative framework will provide optimized allocation of testing resources, risk-averse prediction of effective corrective measures, and privacy guarantees to incentivize data sharing among stakeholders. These results are expected to lead to reduced food safety incidents caused by a variety of pathogens and a model for digital food safety systems that can be applied to a number of food safety related challenges.