Method and system for robust pseudonymization that retains data diversity

A method implemented by one or more processors, the method comprising:

• receiving a query;
• redacting, based on frequent templates stored in a PII-free template database, one or more words in the query not found in the frequent templates as potentially revealing personal identifiable information (PII);
• for each word in the query that is not redacted, determining frequent template sets in the PII-free template database that contain a respective word in the query that is not redacted;
• selecting a frequent template set from the determined frequent template sets that corresponds to a highest occurrence frequency;
• redacting, based on the selected frequent template set, one or more additional words in the query that is not found within the selected frequent template set as potentially revealing PII;
• and
• processing the query having the one or more redacted words and the one or more redacted additional words, using a generative model, to replace the one or more redacted words and the one or more redacted additional words with corresponding PII-free words, resulting in a PII-free query.

A system comprising one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to:

• receive, via a client device and during a human to computer dialog session between a user and an automated assistant, one or more utterances belonging to the human to computer dialog session;
• process the one or more utterances to generate a transcript for each utterance from the one or more utterances;
• for the transcript of each utterance from the one or more utterances:
• identify one or more candidate words in the transcript that potentially convey personally identifiable information (PII),
• determine occurrences of the one or more candidate words in log of reference transcripts generated from historical human-to-computer dialogs, and
• based on the occurrences, flag one or more of the candidate words as not conveying PII;
• redact one or more other words in the transcript based on one or more redacting rules, while preserving the one or more words flagged as not conveying PII, to generate a redacted transcript having one or more redacted slots that correspond to the one or more redacted words;
• process the redacted transcript as input, using a generative model trained based on redacted data, to generate output corresponding to a modified transcript that has the one or more redacted slots of the redacted transcript filled with PII-free content;
• and
• generate one or more training instances based on the modified transcript of each utterance from the one or more utterances.