Capital One Services, LLC

Kishore Doppalapudi

Naveen Teja Koduru

Dennis J Mire

Siva Vegesana

Ramesh Parvataneni

Methods and systems of data de-tokenization are described herein to provide solutions to utilizing tokenized data files. A de-tokenization service controller may extract instances of tokenized data by determining a schema associated with a tokenized file, wherein the schema identifies which fields contain tokenized data. A decryption system may decrypt the tokens and send decrypted sensitive values to the de-tokenization service controller. The de-tokenization service controller may then generate a de-tokenized data file comprising a plurality of records corresponding to the plurality of original tokenized records, using the decrypted sensitive values in place of the instances of tokenized data. In some embodiments, the methods may further comprise generating a validated file by adding one or more fields indicating the results of validation based on a set of validation rules. The methods may further comprise re-tokenizing the de-tokenized data file, before storing the data files again in a re-tokenized data storage.

De-tokenization patterns and solutions

Methods and systems of data tokenization are described herein to provide protection for sensitive data. A tokenization service controller may extract sensitive data by determining a schema, the schema identifying which fields contain sensitive data. A token may be generated corresponding to each instance of the extracted sensitive data. The tokenization service controller may then generate a tokenized data set comprising a plurality of tokenized records arranged according to the same format as the original records, wherein the tokenized records use the generated tokens in place of the corresponding sensitive data.

Batch tokenization service

A system, method, and computer-readable medium for consolidating multiple databases into a single or a smaller number of databases is described. An improved structure of target database may be achieved by removing constraints used when creating legacy databases. According to some aspects, these and other benefits may be achieved by combining multiple source tables into a single target table with errors being identified and addressed during the creation of the instructions to generate the target table. In implementation, this may be effected by converting values from a source table and values from a target table into normalized versions of those values and then comparing the normalized values. A benefit includes verifying that the data from the source table is being properly reflected in the target table.

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Frisco, TX, United States of America

Kishore Doppalapudi