MACHINE LEARNING DATA ANONYMIZER

原文（中文）

Aspects of the present disclosure relate to a machine learning data anonymizer. To anonymize data that is provided for third party processing, sensitive entities are identified therein, which are replaced with replacement entities accordingly. In examples, the replacement entities include an indication of a category corresponding to the sensitive entity, thereby retaining a context/semantic meaning of the sensitive entity without providing the sensitive entity itself. A mapping is generated that associates replacement entities and corresponding sensitive entities, thereby facilitating subsequent deanonymization. Once generated output is received from the third party (e.g., as may have been generated by a machine learning model), the generative output is processed according to the mapping to substitute replacement entities therein with corresponding sensitive entities, thereby generating deanonymized model output in which sensitive entities are reintroduced and thus available for subsequent processing.