US 11,930,023 B2
Deep learning-based similarity evaluation in decentralized identity graphs
Ashish Kundu, Elmsford, NY (US); Arjun Natarajan, Old Tappan, NJ (US); Kapil Kumar Singh, Cary, NC (US); and Joshua F. Payne, San Antonio, TX (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on May 10, 2019, as Appl. No. 16/409,212.
Prior Publication US 2020/0358796 A1, Nov. 12, 2020
Int. Cl. G06F 16/901 (2019.01); G06N 3/08 (2023.01); H04L 9/40 (2022.01)
CPC H04L 63/1425 (2013.01) [G06F 16/9024 (2019.01); G06N 3/08 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
representing a first identity profile from a first network as a first identity graph;
representing a second identity profile from a second network as a second identity graph, wherein the first identity profile and the second identity profile are decentralized identity profiles, wherein the first identity graph and the second identity graph are decentralized identity graphs;
representing a first neighborhood of nodes of the first identity graph as a first neighborhood vector and a second neighborhood of nodes of the second identity graph as a second neighborhood vector;
performing a contrastive loss analysis of the first identity graph to the second identity graph to describe similarities in the first and second neighborhood vectors;
determining a similarity score between the first identity profile and the second identity profile based on the similarities of the first and second identity graphs, wherein the similarity score is across multiple identity profiles represented in the decentralized identity graphs; and
implementing a security action based on the similarity score.