	US 12,388,799 B1
	Systems and methods for providing a split inference approach to protect data and model
Greg Storm, Kansas City, MO (US); Gharib Gharibi, Overland Park, KS (US); and Riddhiman Das, Parkville, MO (US)
Assigned to Selfiie Corporation, Alameda, CA (US)
Filed by TripleBlind, Inc., Kansas City, MO (US)
Filed on Sep. 7, 2022, as Appl. No. 17/939,836.
Application 17/939,836 is a continuation of application No. 17/180,475, filed on Feb. 19, 2021.
Application 17/180,475 is a continuation in part of application No. 16/828,085, filed on Mar. 24, 2020, granted, now 11,582,203.
Application 17/180,475 is a continuation in part of application No. 16/828,216, filed on Mar. 24, 2020.
Application 17/180,475 is a continuation in part of application No. 17/176,530, filed on Feb. 16, 2021.
Application 16/828,085 is a continuation of application No. 16/828,354, filed on Mar. 24, 2020, granted, now 10,924,460, issued on Feb. 16, 2021.
Application 17/180,475 is a continuation in part of application No. 16/828,420, filed on Mar. 24, 2020, granted, now 11,363,002, issued on Jun. 14, 2022.
Application 17/939,836 is a continuation of application No. 17/743,887, filed on May 13, 2022, granted, now 11,531,782.
Application 17/939,836 is a continuation of application No. 17/742,808, filed on May 12, 2022.
Claims priority of provisional application 63/241,255, filed on Sep. 7, 2021.
Claims priority of provisional application 63/020,930, filed on May 6, 2020.
Claims priority of provisional application 62/948,105, filed on Dec. 13, 2019.
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 9/40 (2022.01); G06F 17/16 (2006.01); G06F 18/2113 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06N 3/082 (2023.01); G06Q 20/40 (2012.01); G06Q 30/0601 (2023.01); H04L 9/00 (2022.01); H04L 9/06 (2006.01)

CPC H04L 63/0428 (2013.01) [G06F 17/16 (2013.01); G06F 18/2113 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 3/082 (2013.01); G06Q 20/401 (2013.01); G06Q 30/0623 (2013.01); H04L 9/008 (2013.01); H04L 9/0625 (2013.01); G06Q 2220/00 (2013.01); H04L 2209/46 (2013.01)]

17 Claims

1. A method comprising:

dividing, at a first computing device, a trained model into a first portion of the trained model and a second portion of the trained model;

transmitting the second portion of the trained model to a second computing device, wherein the second computing device provides private data to the second portion of the trained model to generate activation values and wherein the private data is kept secret from the first computing device;

receiving the activation values at the first computing device from the second computing device;

applying the activation values to the first portion of the trained model to yield a trained model output, wherein applying the activation values to the first portion of the trained model is part of a prediction process of predicting the trained model output based on the private data that preservers privacy between the first computing device and the second computing device;

and transmitting the trained model output from the first computing device to the second computing device, wherein computations occurring with the first portion of the trained model on the first computing device and computations occurring with the second portion of the trained model on the second computing device both use secure multi-party computation which implements a Secure Multi function, and a SecureCompare function operating on a Ring R and wherein the Secure Multi function and SecureCompare function enables the first computing device and the second computing device to securely perform multiplication and comparison on their respective portion of the trained model utilizing the Ring R to yield the trained model output.