EDITING A TARGET MODEL TO FORGET DATA SAMPLES USING A REFERENCE MODEL TO ADJUST WEIGHTS OF THE TARGET MODEL

• Main Authors: FARKASH, Ariel, SHMELKIN, Ron, GOLDSTEEN, Abigail, ZOHAR, Tal
• Format: Patent
• Language: English
• Published: 29.08.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

Provided are a computer program product, system, and method for editing a target model to forget data samples. Forget data samples of data samples to forget are inputted into a reference model, trained on a non-private data set, to produce reference output. The forget data samples to forget are inputted to a target model, trained on a total data set comprising the non-private data set and a private data set, to produce target output. The private data set includes the forget data samples A loss function is calculated to measure a divergence of the reference output and the target output. A determination is made of gradients that minimize an error of the loss function. Optimized gradients are calculated from the determined gradients. The optimized gradients are applied to update weights in the target model to produce an edited target model.