Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: Example systems and methods enhance user privacy by performing efficient on-device public-private computation on a combination of public and private data, such as, for example, public and private graph data. In particular, the on-device public-private computation framework described herein can enable a device associated with an entity to efficiently compute a combined output that takes into account and is explicitly based upon a combination of data that is associated with the entity and data that is associated with one or more other entities that are private connections of the entity, all without revealing to a centralized computing system a set of locally stored private data that identifies the one or more other entities that are private connections of the entity.

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: Example systems and methods enhance user privacy by performing efficient on-device public-private computation on a combination of public and private data, such as, for example, public and private graph data. In particular, the on-device public-private computation framework described herein can enable a device associated with an entity to efficiently compute a combined output that takes into account and is explicitly based upon a combination of data that is associated with the entity and data that is associated with one or more other entities that are private connections of the entity, all without revealing to a centralized computing system a set of locally stored private data that identifies the one or more other entities that are private connections of the entity.