Systems and methods for measuring data exfiltration vulnerability and dynamic differential privacy in a zero-trust computing environment

Abstract

An algorithm is trained on a known dataset to facilitate dynamic data exfiltration protection in a zero-trust environment. The classifications generated by the trained algorithm on a very large set of inputs may then be used to train an inversion threat model by a bad actor attempting to exfiltrate data from the data steward. Since our system is taking place within the enclave/secure computing node, the system is able to very accurately build an inversion threat model since the original training dataset is known (a 'gold standard' inversion model). This inversion model can be characterized to determine its performance/accuracy of properly identifying a given input as being within the original training dataset or not (a data exfiltration event). This very accurate inversion model will be superior at data exfiltration as compared to any inversion attack model generated by a bad actor using only the algorithm classification outputs. As such, the results of this inversion model provide a ceiling on the likelihood of data exfiltration. Very accurate inversion models indicate that data exfiltration is easier/more likely. A poor performing inversion model indicates that the training data is more secure and less able to be exfiltrated. Differential privacy may be fluctuated to manage the risk of data exfiltration.