US 12,130,929 B2
Subject level privacy attack analysis for federated learning
Pallika Haridas Kanani, Westford, MA (US); Virendra J. Marathe, Florence, MA (US); Daniel Wyde Peterson, Firestone, CO (US); and Anshuman Suri, Charlottesville, VA (US)
Assigned to Oracle International Corporation, Redwood City, CA (US)
Filed by Oracle International Corporation, Redwood City, CA (US)
Filed on Feb. 25, 2022, as Appl. No. 17/681,638.
Prior Publication US 2023/0274004 A1, Aug. 31, 2023
Int. Cl. G06F 21/57 (2013.01); G06F 21/62 (2013.01)
CPC G06F 21/577 (2013.01) [G06F 21/6245 (2013.01); G06F 2221/033 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one processor;
a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a federated machine learning model analysis system, the federated machine learning model analysis system configured to:
receive, via an interface of the federated machine learning model analysis system, a request that selects an analysis of one or more inference attacks to determine a presence of data of a subject in a training set of a federated machine learning model;
access the federated machine learning model to perform the selected one or more inference attacks to determine the presence of the data of the subject in the training set of the federated machine learning model;
analyze respective inferences produced by the federated machine learning model as part of performing the selected one or more inference attacks to determine respective success measurements for the selected one or more inference attacks; and
provide, via the interface, the respective success measurements for the selected one or more inference attacks.