Iteration 1

The diagram outlines the process of building a machine learning framework through several stages. It emphasizes the importance of data privacy throughout the process. Iteration 1 Process: Explanation is already provided in initial idea section The diagram also raises a question about preserving time while modifying live data, highlighting a consideration for temporal data management. Training Process:

1. Application of Machine Learning Algorithm: An ML algorithm is deployed on non-anonymized data for initial training.
2. Utilization of Results as Metrics: The outcomes from this step serve as metrics for subsequent training processes.
3. Incorporation of Anonymized Training Results as Feedback Metrics: The anonymized training results are utilized as feedback metrics to refine the model further. By iteratively refining the model based on anonymized feedback, the framework maintains data utility and privacy simultaneously