US 12,088,565 B2
Systems and methods for privacy preserving training and inference of decentralized recommendation systems from decentralized data
Gharib Gharibi, Overland Park, KS (US); Greg Storm, Kansas City, MO (US); Ravi Patel, Kansas City, MO (US); Babak Poorebrahim Gilkalaye, Kansas City, MO (US); and Riddhiman Das, Parkville, MO (US)
Assigned to Triplelind Holdings, Inc., Kansas City, MO (US)
Filed by TripleBlind, Inc., Kansas City, MO (US)
Filed on Sep. 7, 2022, as Appl. No. 17/939,585.
Application 17/939,585 is a continuation of application No. 17/743,887, filed on May 13, 2022, granted, now 11,531,782.
Application 17/939,585 is a continuation of application No. 17/742,808, filed on May 12, 2022, granted, now 11,599,671.
Application 17/939,585 is a continuation of application No. 17/180,475, filed on Feb. 19, 2021.
Application 17/743,887 is a continuation in part of application No. 17/176,530, filed on Feb. 16, 2021, granted, now 11,895,220.
Application 17/743,887 is a continuation in part of application No. 16/828,085, filed on Mar. 24, 2020, granted, now 11,582,203.
Application 17/176,530 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 17/180,475 is a continuation in part of application No. 16/828,216, filed on Mar. 24, 2020.
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.
Prior Publication US 2023/0300115 A1, Sep. 21, 2023
Int. Cl. G06F 16/00 (2019.01); 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); H04L 9/40 (2022.01)
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)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
initiating, at a server device, an item-vector matrix V, wherein the item-vector matrix V comprises a value m related to a total number of items across one or more client devices and a value d representing a hidden dimension which is not directly observable from input or output;
transmitting the item-vector matrix V to each client device of a set of client devices, wherein each client device trains a local matrix factorization model comprising a version of a recommendation system using a respective user vector U and the item-vector matrix V to generate a respective set of gradients on each respective client device;
receiving, via a secure multi-party compute protocol to enable parties to perform multiplication and comparison securely, and from each client device of the set of client devices, the respective set of gradients;
updating the item-vector matrix V using the respective set of gradients from each client device to generate an updated item-vector matrix V by aggregating the respective set of gradients from each client device to generate the updated item-vector matrix V; and
downloading the updated item-vector matrix V to at least one client device of the set of client devices.