US 2026/0228545 A1 · PRIVACY-PRESERVING TRAINING DATA TRANSFORMATION FOR MACHINE LEARNING

Abstract

Systems and methods leverage the capabilities of large language models (LLMs) to generate training data from sensitive data without exposing sensitive information in the sensitive data. In some embodiments, sensitive data is fed to the LLM in an automated manner along with a customized prompt asking the LLM to rephrase the text while mutating specific types of entities in specific ways to generalize the content to other entities that are similar. In some examples, in addition to paraphrasing the sensitive data, items in the data can be switched out (effectively anonymizing), for example, entity names, people's names, dates, times, numerical values, etc.

Claims15

Dependent claims

A system for generating training data for large language models (LLMs) comprising:

a memory;
a processor;
and
a non-transitory, computer-readable storage medium storing a set of instructions executable by the processor, the set of instructions comprising instructions for:
receiving input data;
providing a prompt to a large language model (LLM) trained to generate output data, the prompt specifying that source text in the input data be rephrased and specifying that certain indicators in the source text be anonymized;
reviewing, by a human, the generated output data;
and
training another machine learning model using the data thus generated.

6 more elements

Dependencies are inferred from the claim text.

Privacy-preserving training data transformation for machine learning

Life of this patent

Privacy-preserving training data transformation for machine learning

Life of this patent

Abstract

Claims15