Mastercard International Incorporated

Curtis Villars

Todd Christian Lowenberg

Serge Bernard

Andrew S Reiskind

Rohit Chauhan

JoAnn C Stonier

Jason Alex Feldman

A method for double anonymization of data includes: receiving, by a first computing system, a plurality of first data sets, each including a set identifier and personally identifiable information; anonymizing, by the first computing system, each of the first data sets, by hashing the set identifier included in each first data set to obtain a hashed identifier and deidentifying the personally identifiable information; transmitting, by the first computing system, the plurality of anonymized first data sets to a second computing system, wherein the second computing system is distinct and separate from the first computing system; anonymizing, by the second computing system, each of the anonymized first data sets, by hashing the hashed identifier to obtain a double-hashed identifier; and storing, in the second computing system or a third separate and distinct computing system, the plurality of double anonymized first data.

Method and system for double anonymization of data

A method for making a targeted offer to an audience of a population of entities (e.g., social network). The method involves retrieving, from one or more databases, a first set of information including activities and characteristics attributable to a first plurality of entities; generating a plurality of interaction associations based on at least one of selected activities criteria and selected characteristics criteria from the first set of information; and conveying to a third party one or more interaction associations to enable the third party to identify a second set of information including activities and characteristics attributable to a second plurality of entities. The second set of information has matching activities and characteristics to the activities and characteristics of the interaction associations. The second plurality of entities has a propensity to carry out certain activities based on the activities criteria and/or characteristics criteria used in forming the interaction associations, to enable a targeted offer to be made to an audience of the second plurality of entities. A system for making a targeted offer to an audience of a population of entities (e.g., social network).

Method and system for making a targeted offer to an audience

A system and method for optimizing at least one of a promotion, a targeting of the promotion, and a campaign associated with the promotion. Acceptances of the promotion are received following each user's selection of the promotion. Information representing users is received from publisher computing devices. Thereafter, respective match keys are generated that are usable to access information representing transaction accounts of each of the users. The match keys are transmitted computing device(s) having access to information representing transaction accounts. Information representing a selected one account is received. For users who redeemed the promotion, the promotion is associated with the selected one transaction account. Thereafter, as a function of the received selection of the transaction account and/or associating the promotion with a selected one transaction account, the at least one processor optimizes the promotion, a targeting of the promotion to other users, and/or a campaign associated with the promotion.

System and method for card-linked services

A system and method for making a targeted offer to an audience of a population of entities involves: retrieving a first set of information attributable to a first plurality of entities; generating a plurality of interaction associations from the first set of information; and conveying to a third party one or more interaction associations to enable the third party to identify a second set of information attributable to a second plurality of entities. The second set of information has matching activities and characteristics to the activities and characteristics of the interaction associations. The second plurality of entities has a propensity to carry out certain activities based on the activities criteria and/or characteristics criteria used in forming the interaction associations, to enable a targeted offer to be made to an audience of the second plurality of entities.

A method for generating microsegments includes retrieving a first set of information including activities attributable to a first plurality of entities and first entity identifiers; retrieving a second set of information including characteristics attributable to the first plurality of entities and second entity identifiers; combining the first and second sets of information using the first and second entity identifiers to obtain a combined set of information, the combined set of information not including personally identifiable information; generating a plurality of microsegments based on selected activities and/or characteristics criteria from the combined set of information; receiving a third set of information including characteristics attributable to a second plurality of entities; and matching characteristics in respective microsegments to characteristics of entities in the second plurality of entities to identify an audience of entities that have a propensity to carry out certain activities based on the selected activities and/or characteristics.

Protecting privacy in audience creation

A method for obfuscating granular transaction data via the introduction of noise includes: storing transaction data entries, each including transaction data values including at least a transaction amount and merchant identifier; receiving a data request including selection criteria and desired metrics; identifying a subset of transaction data entries based on the selection criteria; identifying a metric value for each desired metric based on the corresponding transaction data value in each of the transaction data entries in the subset; inserting noise to modify the subset by (i) adjusting the transaction data values included in at least two of the transaction data entries, and/or (ii) adding at least one transaction data entry to and removing at least one transaction data entry from the subset, where the inserted noise adjusts the metric values within a predetermined range; and transmitting the modified subset.

Method and system for obfuscation of granular data while retaining data privacy

A method for combining disparate data sets using dynamic de-identifiers includes: receiving a first data set from a first external entity, the data set including first data entries including attribute values, and wherein the first data set does not include any personally identifiable information; receiving a second data set from a second external entity, the data set including second data entries including attribute values, and wherein the second data set does not include any personally identifiable information; identifying a dynamic de-identifier (DDID) for each first data entry and second data entry; matching each second data entry to a first data entry based on a correspondence between the associated attribute values; identifying insights for at least one first data entry based on at least the attribute values included in the corresponding matched second data entry; and transmitting at least the identified insights to the first external entity.

Method and system for dynamic de-identification of data sets

A method for maintaining consumer privacy in behavioral scoring includes a first computing system and a second computing system. The first computing system disguises consumer characteristics and maps disguised consumer characteristics to unencrypted account identifiers, and then transmits the data to the second computing system. The second computing system encrypts the account identifiers upon receipt, and maps the encrypted account identifiers to anonymous transaction data. The second computing system uses the transaction data to calculate consumer behavioral scores, and then generates a scoring algorithm that uses disguised consumer characteristics to calculate consumer behavior scores based on the calculated consumer behavioral scores and corresponding disguised consumer characteristics. The generated algorithm is then returned to the first computing system, with the second computing system not receiving any unencrypted account identifiers, any undisguised consumer characteristics, or any personally identifiable information.

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Chatham, NJ, United States of America

Curtis Villars