Patent ID: 11947699
Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A computing system configured for securing data access of machine learning training data at a plurality of distributed computing devices, wherein the computing system comprises:
one or more processors; and
one or more computer readable hardware storage devices that store computer-executable instructions that are structured to be executable by the one or more processors to cause the computing system to at least:
receive electronic content comprising original data corresponding to a determined security level;
selectively divide the electronic content into a plurality of microsegments by duration, the duration of the microsegments being selected according to the determined security level associated with the electronic content;
identify a plurality of destination computing devices configured to apply a plurality of labels corresponding to the plurality of microsegments;
restrictively distribute the plurality of microsegments to the plurality of destination computing devices, wherein the restricted distribution includes restricting a quantity of microsegments associated with the original data to be distributed to any one destination computing device of the plurality of destination computing devices to less than a pre-determined threshold, wherein the pre-determined threshold is determined based on the determined security level;
receive a plurality of labels corresponding to plurality of microsegments divided from the electronic content, the plurality of labels being provided by at least two of the plurality of destination computing devices;
reconstruct the plurality of microsegments into a reconstructed electronic content comprising the plurality of labels corresponding to the plurality of microsegments, wherein the reconstructed electronic content further comprises microsegments that are divided to include portions of microsegments that overlap each other, wherein the reconstructed electronic content comprises training data for a machine learning model;
determine that at least one overlapped portion of microsegments includes a set of non-equivalent corresponding labels; and
selecting a particular label of the set of non-equivalent corresponding labels for inclusion in the reconstructed electronic content for the overlapped portion of microsegments.