Patent ID: 11875351
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A mobile device, comprising:
memory; and
a logic circuitry coupled with the memory, the logic circuitry to:
receive, from a server, a customer identification to associate the mobile device to a pretrained model, the pretrained model is trained to identify fraudulent transactions;
receive, from the server, a cluster identification to associate the mobile device to a first cluster that comprises a plurality of customers, wherein a first customer associated with the mobile device is one of the plurality of customers;
receive transaction data for an anonymized customer from the first cluster, the transaction data to describe a purchase made by the anonymized customer;
determine, using the model, based on the transaction data, a vote indicative whether the purchase is fraudulent or that the purchase is non-fraudulent, wherein the model is pretrained to detect fraudulent transactions based on a purchase history of the plurality of customers in the first cluster;
communicate, in response to a receipt of the transaction data, a message comprising the vote to indicate that the purchase is fraudulent or that the purchase is non-fraudulent based on the transaction data, the message to identify the transaction data with encoded data; and
update the model based on an indication from the server, wherein the server determines that the purchase is fraudulent or that the purchase is non-fraudulent based on a plurality of votes from a plurality of customer devices, wherein updating of the model includes:
randomly selecting a plurality of transactions associated with the plurality of customers, including receiving another transaction data associated with the plurality of transactions from a plurality of customer devices associated with the plurality of customers in the first cluster;
modifying and encrypting at least a portion of each received another transaction data associated with the plurality of transactions; and
executing at least one of supervised or unsupervised training of the model using the modified and encrypted at least a portion of each another transaction data associated with the plurality of transactions.