Artificial intelligence (ai) model trained using clustering and reinforcement learning for data de-identification engine

Abstract

The disclosed system and method provide an artificial intelligence (AI) model trained using clustering and reinforcement learning. Data in a dataset can be loaded for de-identification, along with data scope answers. The data can be audited, and once audited, the audited data and the data scope answers can be provided to a strategy recommendation engine including the trained AI model. The engine can determine a cluster corresponding to the dataset and assesses strategies for data de-identification based on the determined cluster. The strategies can be ranked and provided as output, providing the ability to better de-identify the dataset by indicating which techniques will be the most effective. Additionally, the system and method can automatically implement a top-ranked strategy satisfying certain criteria as a determined optimal approach for data de-identification. Clustering and reinforcement learning may efficiently and automatically glean information from unlabeled data. Feedback-based retraining may improve performance further.