Abstract: A system and method may facilitate data matching between multiple data owners through the use of a common set of “pseudonymized” individuals or household identifiers without requiring any data owner to share personally identifiable information with any other party. Data owners may accurately match ID-level online and offline data across multiple platforms with other participants' online and offline data without requiring any party to disclose personally identifiable information about their users or households. This use of deterministic data, rather than probabilistic data or a combination of deterministic and probabilistic data, is more accurate while also lowering the risk of data breach since no personally identifiable information is shared.

Abstract: A system and method may facilitate data matching between multiple data owners through the use of a common set of “pseudonymized” individuals or household identifiers without requiring any data owner to share personally identifiable information with any other party. Data owners may accurately match ID-level online and offline data across multiple platforms with other participants' online and offline data without requiring any party to disclose personally identifiable information about their users or households. This use of deterministic data, rather than probabilistic data or a combination of deterministic and probabilistic data, is more accurate while also lowering the risk of data breach since no personally identifiable information is shared.

Abstract: Systems and methods are described for distilling data. First data associated with a user may be received. The first data associated with the user may comprise an anonymized hash of an identifier associated with the user. A database may be determined to comprise a first record indicating the anonymized hash. The first record may comprise second data associated with the user. Based on the determining that the database comprises the first record, a second record may be generated. The second record may comprise the first data associated with the user, the second data associated with the user, and the anonymized hash. Based on the determining that the database comprises the first record, the example method may be stored to the database. These and other user and/or data distillation methods and systems are described herein.

Abstract: Systems and methods are described for distilling data. First data associated with a user may be received. The first data associated with the user may comprise an anonymized hash of an identifier associated with the user. A database may be determined to comprise a first record indicating the anonymized hash. The first record may comprise second data associated with the user. Based on the determining that the database comprises the first record, a second record may be generated. The second record may comprise the first data associated with the user, the second data associated with the user, and the anonymized hash. Based on the determining that the database comprises the first record, the example method may be stored to the database. These and other user and/or data distillation methods and systems are described herein.

Abstract: Systems and methods are described for a reputation-based consensus protocol. A reputation score of a first node of a plurality of nodes may be determined. A distributed ledger record associated with a second node of the plurality of nodes may be received. The distributed ledger record may be stored to a distributed ledger based on the first node validating the distributed ledger record and based on the reputation score.

Abstract: Systems and methods are described for a reputation-based consensus protocol. A reputation score of a first node of a plurality of nodes may be determined. A distributed ledger record associated with a second node of the plurality of nodes may be received. The distributed ledger record may be stored to a distributed ledger based on the first node validating the distributed ledger record and based on the reputation score.

Abstract: Systems and methods are described for distilling data. First data associated with a user may be received. The first data associated with the user may comprise an anonymized hash of an identifier associated with the user. A database may be determined to comprise a first record indicating the anonymized hash. The first record may comprise second data associated with the user. Based on the determining that the database comprises the first record, a second record may be generated. The second record may comprise the first data associated with the user, the second data associated with the user, and the anonymized hash. Based on the determining that the database comprises the first record, the example method may be stored to the database. These and other user and/or data distillation methods and systems are described herein.

Abstract: Systems and methods are described for a reputation-based consensus protocol. A reputation score of a first node of a plurality of nodes may be determined. A distributed ledger record associated with a second node of the plurality of nodes may be received. The distributed ledger record may be stored to a distributed ledger based on the first node validating the distributed ledger record and based on the reputation score.

Abstract: Systems and methods are described for distilling data. First data associated with a user may be received. The first data associated with the user may comprise an anonymized hash of an identifier associated with the user. A database may be determined to comprise a first record indicating the anonymized hash. The first record may comprise second data associated with the user. Based on the determining that the database comprises the first record, a second record may be generated. The second record may comprise the first data associated with the user, the second data associated with the user, and the anonymized hash. Based on the determining that the database comprises the first record, the example method may be stored to the database. These and other user and/or data distillation methods and systems are described herein.

Abstract: Systems and methods are described for a reputation-based consensus protocol. A reputation score of a first node of a plurality of nodes may be determined. A distributed ledger record associated with a second node of the plurality of nodes may be received. The distributed ledger record may be stored to a distributed ledger based on the first node validating the distributed ledger record and based on the reputation score.

Abstract: Systems and methods are described for distilling data. First data associated with a user may be received. The first data associated with the user may comprise an anonymized hash of an identifier associated with the user. A database may be determined to comprise a first record indicating the anonymized hash. The first record may comprise second data associated with the user. Based on the determining that the database comprises the first record, a second record may be generated. The second record may comprise the first data associated with the user, the second data associated with the user, and the anonymized hash. Based on the determining that the database comprises the first record, the example method may be stored to the database. These and other user and/or data distillation methods and systems are described herein.

Abstract: Systems and methods are described for a reputation-based consensus protocol. A reputation score of a first node of a plurality of nodes may be determined. A distributed ledger record associated with a second node of the plurality of nodes may be received. The distributed ledger record may be stored to a distributed ledger based on the first node validating the distributed ledger record and based on the reputation score.