Meta Platforms, Inc.

Facebook, Inc.

Andrew Knox

Michael Randolph Corey

Erik Taubeneck

Sanjay Kanaka Sai Tirupattur Saravanan

William Patrick Hesch

Gad Alon

Cameron Jones

Toby Jonas F Roessingh

Systems, methods, and non-transitory computer-readable media can determine a first dataset provided by a first party, wherein the first dataset includes a set of vectors that are each associated with a user identifier. A second dataset provided by a second party can be determined, wherein the second dataset includes a set of vectors that are each associated with a user identifier. One or more vectors in the first dataset can be matched to vectors in the second dataset based on a secure multi-party computation without revealing respective graph information of the first party or the second party. Respective mappings between vectors in the first dataset to a set of shared universal identifiers can be provided to the first party. Respective mappings between vectors in the second dataset to the set of shared universal identifiers can be provided to the second party.

Systems and methods for securely sharing and processing data between parties

A content publisher may transfer an impression token value to a user in response to the content publisher displaying an advertisement for a merchant to the user. The content publisher may record the transfer on a zero knowledge blockchain. The merchant may transfer a conversion token value to the user in response to the user making a purchase from the merchant. The merchant may record the transfer on the zero knowledge blockchain. The user may transfer the impression token value and the conversion token value to a measurement company. The measurement company may calculate attribution and lift results for the advertisement.

Attribution of conversion made by a client using impression block and conversion block

Systems, methods, and non-transitory computer-readable media can generate individual feature data for each user of a plurality of users. A first cohort comprising a first plurality of users is generated, wherein the first plurality of users are selected from the plurality of users based on the individual feature data. A first set of cohort feature data associated with the first cohort is generated based on individual feature data for the first plurality of users. The first set of cohort feature data and a first set of cohort membership information are transmitted to a modeler. The first set of cohort membership information identifies each user of the plurality of users.

Systems and methods for sensitive data modeling

Systems, methods, and non-transitory computer-readable media can determine a set of mappings between vectors in a first dataset associated with a first party to a set of shared universal identifiers based on a secure multi-party computation. A set of mappings can be determined between vectors in a second dataset associated with a second party to the set of shared universal identifiers based on the secure multi-party computation. Membership information for each vector in the first dataset can be obtained. The membership information indicating whether an individual associated with the vector is assigned to a test group, a control group, or neither. Conversion information for each vector in the second dataset can be obtained. The conversion information indicating whether an individual converted. Conversion counts for the test group and the control group can be determined based at least in part on the membership information and the conversion information.

Secure multi-party computations may be used to get attribution results without compromising user privacy. A content provider and an advertiser may each sign a calculation indicating that they wish to share data using a secure multi-party computation. A measurement company may sign the calculation indicating that the measurement company has evaluated the computation and that the computation will protect user privacy. A user device may confirm with the verification service that all parties have signed the calculation. The user device may transmit one-time identifiers to the measurement company, which allows impression data and conversion data stored by the content provider and the advertiser to be linked. The content provider, the advertiser, and the measurement company may perform the secure multi-party computation, which allows the advertiser to evaluate attribution results without accessing the user data stored by the content provider.

Secure multi-party computation attribution

An online system receives a list of third party user identifiers from a third party attribution system, each associated with one or more user action events by each of a set of users across multiple content providers. The online system determining if it has its own user identifiers that match to at least a subset of the received third party user identifiers. The online system determines relevant impressions corresponding to the matching online system user identifiers, and sends them to the third party attribution system along with an associated subset of the third party user identifiers.

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Brooklyn, NY, United States of America

Andrew Knox