| CPC G06F 21/44 (2013.01) [G06N 20/20 (2019.01); H04L 9/3213 (2013.01)] | 20 Claims |
|
1. A system for updating a federated learning model to identify sensitive information using client-prestored sensitive tokens, the system comprising a computer system that comprises one or more processors programmed with computer program instructions that, when executed, cause the computer system to perform operations comprising:
sending, to a client computing device, a distributed instance of a machine learning model and a client-side training application for training the distributed instance;
sending, to the client-side training application, predetermined sensitive tokens associated with predetermined sensitivity levels, the predetermined sensitive tokens comprising a first sensitive token associated with a first predetermined sensitivity level of the predetermined sensitivity levels, wherein the client-side training application is configured to cause the client computing device to perform operations comprising:
storing, via the client-side training application, the predetermined sensitive tokens associated with the predetermined sensitivity levels at an encrypted data storage of the client computing device;
detecting, based on the predetermined sensitive tokens stored at the client computing device, a user input comprising the first sensitive token and first surrounding tokens around the first sensitive token in the user input, the user input being provided into a user interface of another client-side application executing at the client computing device;
training the distributed instance of the machine learning model based on the user input and the first predetermined sensitivity level for the first sensitive token by:
providing the user input comprising the first sensitive token and a set of surrounding tokens of the first sensitive token to a client model instance of the machine learning model to obtain a first predicted sensitivity level for the first sensitive token; and
updating weights of hidden layers of the client model instance based on an assessment of the first predicted sensitivity level against the first predetermined sensitivity level; and
updating the machine learning model based on the updated weights of the hidden layers of the client model instance by obtaining the updated weights of the hidden layers of the client model instance from the client computing device.
|