Abstract: 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.

Abstract: 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.

Abstract: 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 anyonmized 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: Methods and systems for determining consumer spend analytics are provided. The method includes identifying a plurality of microsegments of a population, retrieving transaction data associated with a first cardholder from a payment processing network, and matching the first cardholder to a first microsegment of the plurality of microsegments. The method also includes calculating, based at least in part on the typical income and the typical spend of the consumers in the first microsegment, a cardable spend for the first cardholder. The method further includes calculating, based at least in part on the cardable spend and the transaction data, a carded spend share that the first cardholder spent using a first payment device over the payment processing network; determining at least one consumer spend analytic based on the carded spend share; and reporting the carded spend share and the at least one consumer spend analytic.

Abstract: A system, method, and computer-readable storage medium configured to maintain account continuity when payment account numbers change.

Abstract: 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.

Abstract: 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

Abstract: A method for generating indices of activity for geographic areas includes: receiving a plurality of activity values, each associated with an entity and geographic location; identifying a geographic grid, the grid including a plurality of geographic areas area of equal size and including zero or at least two geographic locations associated with an activity value; calculating a weight value for each geographic area, the weight value being based on at least (i) a first weight factor applied to each activity value in the respective geographic area and (ii) a second weight factor applied to each activity value associated with a geographic location in an adjacent geographic area; calculating an index value for each geographic area, the index value being based on the calculated weight value and an average weight value for each geographic area; and transmitting at least the calculated index value for one or more geographic areas.

Abstract: 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.

Abstract: 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.