Bank of America Corporation

Aeric John Solow

Manu Jacob Kurian

Ganesh Bonda

Jayachandra Varma

Albena Noveva Fairchild

Harinath Meedinti Bhaskara Reddy

Erica Perkins

Ion Freeman

Sasikumar Purushothaman

A method for determining whether an existing customer is eligible for a zero-knowledge proof ('ZKP') application is provided. The method may include determining that the existing customer possesses a private key linked to an account profile associated with the existing customer absent provision of the private key. The determining may also include executing a behavior-scan on the account profile to confirm that a score of the behavior-scan is equal to or greater than a pre-determined score. In response to the determining that the existing customer is eligible for the ZKP application, the method may include generating the ZKP application by executing an encryption algorithm on the sensitive data of the account profile to output a digital zero-knowledge token for each of the sensitive data and auto-filling each input field in the ZKP application that is associated with sensitive data with the digital zero-knowledge token.

Preventing unauthorized exposure of sensitive data

A method for enabling continuity of access to a primary dataset stored in a computer network is provided. The method utilizes a node, a computer processor, and non-transitory computer-readable media storing computer-executable instructions. The node is connected to the computer network. The method includes configuring the mobile device for wireless connection to the computer network. The method includes configuring the mobile device to receive indication that the node is disconnected from the computer network and transmit a copy of the primary dataset or a portion thereof from the computer network to the node. The method includes configuring the node to enable operations on the dataset copy and keep a record of them. The method includes configuring the node to receive indication that the node is connected to the computer network and transmit the record to the computer network.

Encrypted distributed database on a mobile device

Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Self-organization of data storage

Arrangements for controlling data relations and data redundancies are presented. In some aspects, data may be received from a plurality of sources. The data may then be analyzed to determine a score or value associated with the data. Machine learning may be used to analyze the data and/or determine the score associated with the data. In some examples, a type of data may be identified. Based on the type of data, and the data, one or more connections to other data or types of data may be identified. In some examples, the data, as well as any identified connections, any determined score, and the like may be stored in a data container associated with the identified type of data. Stored data may be analyzed to determine whether one or more redundancies exist. If one or more redundancies are identified, one or more data redundancy actions may be identified and executed.

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Richardson, TX, United States of America

Aeric John Solow