Data clean room with exportable, privacy-preserving labels

A system, comprising:

• one or more processors;
• and
• memory storing executable instructions that, as a result of execution by the one or more processors, cause the system to:
• obtain, from a computing entity associated with a multi-party data collaboration effort:
• at least a portion of a dataset comprising sensitive data associated with a set of subjects;
• and
• at least one privacy parameter;
• store the dataset in a data clean room, such that the sensitive data is unexportable from the data clean room;
• select, from one or more plurality of privacy-preserving algorithms and based at least in part on the at least one privacy parameter, a privacy-preserving algorithm;
• and
• use the dataset to train a machine-learning model according to the privacy-preserving algorithm.

A non-transitory computer-readable storage medium storing executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:

• obtain, from a first computing entity, a request to generate a training dataset, the request comprising subject identification information indicative of a first set of subjects;
• query a database that stores records for a second set of subjects associated with a second computing entity, to determine:
• a set of subjects, including at least an overlapping set of subjects that are in both the first and second sets of subjects;
• and
• a set of private labels for the set of subjects;
• obtain, from the second computing entity, one or more privacy parameters;
• select, from a plurality of privacy-preserving algorithms and based at least in part on the one or more privacy parameters, a privacy-preserving algorithm;
• use a privacy-preserving model trained according to the selected privacy-preserving algorithm to infer a corresponding set of privacy-preserving labels for the overlapping set of subjects;
• and
• determine the training dataset, wherein the training dataset comprises the overlapping set of subjects and the corresponding set of privacy-preserving labels.