Deep learning-based detection and data loss prevention of image-borne sensitive documents

Abstract

The technology disclosed relates to distributing a trained master deep learning (DL) stack with stored parameters to a plurality of organizations, to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents. Disclosed is providing organizations with a DL stack update trainer, under the organizations' control, configured to allow the organizations to perform update training to generate updated DL stacks, without the organizations forwarding images of organization-sensitive training examples, and to save non-invertible features derived from the images, ground truth labels for the images, and parameters of the updated DL stacks. In particular, the technology disclosed relates to receiving, from a plurality of the DL stack update trainers, organization-specific examples including the non-invertible features of the organization-sensitive training examples and the ground truth labels, and using the received organization-specific examples to update the trained master DL stack.