US 12,306,859 B2
Method and system for protecting and removing private information used in large language models
Vijay Madisetti, Alpharetta, GA (US); and Arshdeep Bahga, Chandigarh (IN)
Assigned to Vijay Madisetti, Alpharetta, GA (US)
Filed by Vijay Madisetti, Alpharetta, GA (US)
Filed on Jun. 14, 2024, as Appl. No. 18/744,199.
Application 18/744,199 is a continuation in part of application No. 18/406,906, filed on Jan. 8, 2024, granted, now 12,158,904.
Application 18/406,906 is a continuation in part of application No. 18/470,487, filed on Sep. 20, 2023, granted, now 12,147,461.
Application 18/470,487 is a continuation of application No. 18/348,692, filed on Jul. 7, 2023, granted, now 12,001,462.
Claims priority of provisional application 63/551,548, filed on Feb. 9, 2024.
Claims priority of provisional application 63/604,909, filed on Dec. 1, 2023.
Claims priority of provisional application 63/604,910, filed on Dec. 1, 2023.
Claims priority of provisional application 63/602,675, filed on Nov. 27, 2023.
Claims priority of provisional application 63/469,571, filed on May 30, 2023.
Claims priority of provisional application 63/463,913, filed on May 4, 2023.
Prior Publication US 2024/0411789 A1, Dec. 12, 2024
Int. Cl. G06F 16/3329 (2025.01); G06F 40/284 (2020.01)
CPC G06F 16/3329 (2019.01) [G06F 40/284 (2020.01)] 24 Claims
OG exemplary drawing
 
1. A method for generating adversarial data for use in a large language model (LLM) comprising:
receiving an input condition at a generator neural network;
generating synthetic data responsive to the input condition by the generator neural network, a distribution of the synthetic data being configured to diverge from a distribution of authentic data;
receiving each of the input condition, the synthetic data, and the authentic data at a discriminator neural network; and
classifying each of the synthetic data and the authentic data as being one of authentic or synthetic by the discriminator neural network.