Systems and methods for dataset recommendation in a zero-trust computing environment

A zero-trust computing system for recommending a dataset, the system comprising:

• a data store for receiving a set of dataset requirements as a required vector set;
• and
• a runtime server hosted within a sequestered enclave comprising a virtual machine running on secure computing processors for generating historical vector sets, wherein each vector set corresponds to a known dataset, querying the historical vector sets;
• calculating the difference between the required vector set and each of the historical vector sets;
• aggregating a plurality of permutations of historical vector sets into aggregated vectors, wherein the aggregating combines the historical vector sets into the aggregated vector by appending aggregated classes together, selecting the aggregated vector via an optimization, wherein the optimization is responsive to a minimized distance between the aggregated vector and the required vector set, wherein the distance is a calculated difference, identifying a patient cohort from the aggregated vector, brokering contact between a data consumer and a data steward to enable access to the identified patient cohort, wherein the access includes at least one of research on the patient cohort, screening the patient cohort for a pathology, screening the patient cohort for a medical intervention.

A computer program products stored on non-volatile computer memory which when executed upon a computer system performs the steps of:

• receiving a set of dataset requirements as a required vector set;
• generating historical vector sets, wherein each vector set corresponds to a known dataset by encoding the dataset according to a set of classes, generating a matrix of the encoded dataset and converting the generated matrix into a series of points in a vector space, and wherein the generating the historical vector sets is performed in a sequestered enclave comprising a virtual machine running on secure computing processors;
• querying the historical vector sets;
• aggregating a plurality of permutations of historical vector sets into aggregated vectors, wherein the aggregating combines the historical vector sets into the aggregated vector by appending aggregated classes together;
• selecting the aggregated vector via an optimization, wherein the optimization is responsive to a minimized distance between the aggregated vector and the required vector set, wherein the distance is a calculated difference;
• identifying a patient cohort from the aggregated vector;
• brokering contact between a data consumer and a data steward to enable access to the identified patient cohort, wherein the access includes at least one of research on the patient cohort, screening the patient cohort for a pathology, screening the patient cohort for a medical intervention.