US 2025/0094569 A1 · DATA SECURITY MECHANISMS IMPLEMENTED USING MACHINE LEARNING PREDICTORS

Abstract

Aspects and embodiments herein pertain to global ML prediction applied in a data security context. In some examples, a global prediction pertaining to a data item as a whole (the ‘original’ data item) is generated using an ML predictor. A data element is masked by removing it from the data item, or obscuring it in some way (e.g., through modification or replacement). The same ML predictor is applied to the perturbed data item. A change in global confidence between the original data item and the perturbed data item denotes local significance of the individual data item (or subset of data items) that have been masked. Other significance indicators (such as start and end markers of significant sections) are also considered. Other methods of estimating local significance are considered. In some examples, one or more such methods are used to generate training data for training a self-interpretation ML model.

Claims20

Dependent claims

A computer system comprising:

at least one memory configured to store computer-readable instructions;
and
at least one hardware processor coupled to the at least one memory, and configured to execute the computer-readable instructions, which upon execution cause the at least one hardware processor to implement operations comprising:
receiving a data item;
inputting the data item to a machine learning (ML) predictor, resulting in a prediction pertaining to the data item as a whole, and a confidence score pertaining to the prediction;
masking a data element in the data item, resulting in a perturbed data item;
inputting the perturbed data item to the ML predictor, resulting in a perturbed prediction pertaining to the data item as a whole, and a perturbed confidence score pertaining to the prediction;
assigning to the data element, based on the confidence score and the perturbed confidence score, a significance score denoting significance of the data element to the prediction;
and
performing a data security action based on the significance score assigned to the data element.

7 more elements

A computer-implemented method, comprising:

receiving a training item and a groundtruth classification output associated with the training item;
determining using an interpretation process a first groundtruth importance indicator associated with a first training element of each training item, the first groundtruth importance indicator denoting importance of the first training element to the groundtruth classification output, and a second groundtruth importance indicator associated with a second training element of each training item, the second groundtruth importance indicator denoting importance of the second training element to the groundtruth classification output;
and
training a self-interpretation model based on the training item, the first groundtruth importance indicator and the second groundtruth importance indicator, resulting in a trained self-interpretation model configured to compute from an input item a first predicted importance indicator associated with a first element of the input item and a second predicted importance indicator associated with a second element of the input item.

1 more element

A computer-readable storage medium embodying computer-readable instructions, which upon execution on at least one hardware processor, cause the at least one hardware processor to implement operations comprising:

receiving a training item;
generating using a classifier applied to the training item a predicted classification output;
determining using an interpretation process a first groundtruth importance indicator associated with a first training element of each training item, the first groundtruth importance indicator denoting importance of the first training element to the predicted classification output, and a second groundtruth importance indicator associated with a second training element of each training item, the second groundtruth importance indicator denoting importance of the second training element to the predicted classification output;
and
training a self-interpretation model based on the training item, the first groundtruth importance indicator and the second groundtruth importance indicator, resulting in a trained self-interpretation model configured to compute from an input item a first predicted importance indicator associated with a first element of the input item and a second predicted importance indicator associated with a second element of the input item.

2 more elements

Dependencies are inferred from the claim text.

Data security mechanisms implemented using machine learning predictors

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Data security mechanisms implemented using machine learning predictors

Life of this patent

Abstract

Claims20