Method and system for robust pseudonymization that retains data diversity

The method and system for robust pseudonymization in automated assistants address the challenge of processing PII-containing user queries by removing PII through redaction and replacement with PII-free content, enhancing privacy, resource efficiency, and data diversity.

WO2025059495A1 Publication Date: 2025-03-20GOOGLE LLC
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Patent Information

Application Number
PCT/US2024/046663
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2023-11-07
Filing Date
2024-09-13
Publication Date
2025-03-20

AI Technical Summary

Technical Problem

Automated assistants face challenges in processing user queries that contain personally identifiable information (PII), as this information can be exposed to human annotators during training, leading to privacy concerns and the need for strict data wipeout rules.

Method used

A method and system for robust pseudonymization that retains data diversity, which processes user queries and other data to remove PII by identifying candidate words, flagging non-PII words, redacting PII words, and using a generative model to fill redacted slots with PII-free content, thereby creating pseudonymized data suitable for training machine learning models.

Benefits of technology

This approach effectively reduces the risk of privacy issues, conserves computing resources, and lowers labor costs associated with data filtering, while ensuring data diversity and facilitating personalized user experiences.

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Abstract

Method(s) of determining frequent templates (e.g., single tokens / words used by enough distinct users) and frequent template sets (permutations of the frequent templates) for storage in a PII-free template database are provided, where the frequent template sets can be derived from frequent templates and combined thereof. The frequent template sets can also be indexed with IDs for the frequent template sets, where the IDs are stored in the PII-free template database in association with the frequent template sets. Method of redacting a query is also provided, where the frequent templates and / or the frequent template sets in the PII-free template database can be applied to redact one or more words in a query that potentially reveal personal identifiable information (PII). The query with one or more redacted words can be processed, using a generative model, to generate a PII-free query, for use to train the generative model or other machine learning models.
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