US 12,769,845 B2 · 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.

Claims18

Dependent claims

A computerized method of data exfiltration analysis in a zero-trust computing environment, the method comprising:

receiving a data set within a secure computing node;
splitting the data set into a plurality of orthogonal subsets;
training a plurality of models on each of the plurality of orthogonal subsets;
combining each of the models using a weighted average into an algorithm;
iteratively training the algorithm on the data set to generate a set of weights, wherein each iteration of the training improves model performance, and wherein the iterations are concluded when the model performance improvement between a current model and a prior model is below an improvement threshold, and wherein a final model generated after the iterations is a trained algorithm;
training an inversion model on the trained algorithm;
determining a degree of sensitivity for the data set;
calculating an upper limit of practical data exfiltration based upon inversion model complexity and algorithm complexity and input availability
characterizing the performance of the inversion model;
setting a dynamic threshold that is proportional to the degree of sensitivity of the data set and the upper limit of practical data exfiltration;
comparing the inversion model performance against the dynamic threshold;
and
injecting noise into the data set and retraining the algorithm on the noise injected data set and repeating the noise injection until the inversion model performance is below the dynamic threshold.

10 more elements

A zero-trust computer system for data exfiltration analysis comprising:

a database for receiving a data set within a secure computing node;
a runtime server, comprising a processor, for splitting the data set into a plurality of orthogonal subsets, training a plurality of models on each of the plurality of orthogonal subsets, combining each of the models using a weighted average into an algorithm, iteratively training the algorithm on the data set to generate a set of weights, wherein each iteration of the training improves model performance, and wherein the iterations are concluded when the model performance improvement between a current model and a prior model is below an improvement threshold, and wherein a final model generated after the iterations is a trained algorithm;
the runtime server further configured for determining a degree of sensitivity for the data set, and calculating an upper limit of practical data exfiltration based upon inversion model complexity and algorithm complexity and input availability;
and
a threat vulnerability server, comprising a processor, for training an inversion model on the trained algorithm, setting a dynamic threshold that is proportional to the degree of sensitivity of the data set and the upper limit of practical data exfiltration characterizing the performance of the inversion model, comparing the inversion model performance against the dynamic threshold, and inject noise into the data set and retrain the algorithm on the noise injected data set and repeating the noise injection until the inversion model performance is below the dynamic threshold.

2 more elements

Dependencies are inferred from the claim text.

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

Life of this patent

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

Life of this patent

Abstract

Claims18