Machine-learning apparatus and technique

1 . A privacy-aware model-based machine learning engine comprising:

a dispatcher component responsive to receipt of a data request from an open model-based machine learning engine to initiate data capture;

a data capture component responsive to the dispatcher component to capture data comprising sensitive and non-sensitive data to a first dataset;

a sensitive data detector component operable to scan the first dataset to detect the sensitive data;

a sensitive data obscuration component responsive to the sensitive data detector component to create an obscured representation of the sensitive data to be stored with the non-sensitive data in a second dataset; and

a delivery component operable to deliver the second dataset to the open model-based machine learning engine.

2 . The privacy-aware model-based machine learning engine of claim 1 , further comprising a test model component operable to perform machine learning using the first dataset as input.

3 . The privacy-aware model-based machine learning engine of claim 2 , further comprising a comparator component operable to accept as inputs at least one outcome of the test model component and an outcome of a model derived by machine learning from the second dataset.

4 . The privacy-aware model-based machine learning engine of claim 3 , the comparator component further operable to produce non-sensitive accuracy data.

5 . The privacy-aware model-based machine learning engine of claim 4 , the comparator component further operable to deliver the non-sensitive accuracy data to the open model-based machine learning engine.

6 . The privacy-aware model-based machine learning engine of claim 5 , operable in response to detection of inaccuracy to initiate retraining of at least one of said sensitive data detector component or said sensitive data obscuration component.

7 . The privacy-aware model-based machine learning engine of claim 5 , operable in response to detection of inaccuracy to initiate retraining of said model-based machine learning engine.

8 . A method of operating a privacy-aware model-based machine learning engine comprising:

receiving a data request from an open model-based machine learning engine to initiate data capture;

responsive to receiving the data request, capturing data comprising sensitive and non-sensitive data to a first dataset;

scanning the first dataset to detect the sensitive data;

responsive to detecting the sensitive data, creating an obscured representation of the sensitive data to be stored with the non-sensitive data in a second dataset; and

delivering the second dataset to the open model-based machine learning engine.

9 . The method of claim 8 , further comprising performing machine learning using the first dataset as input to a test model to derive a test model outcome.

10 . The method of claim 9 , further comprising operating a comparator component to accept as inputs at least one said test model outcome and an outcome of a model derived by machine learning from the second dataset.

11 . The method of claim 10 , further comprising operating the comparator component to produce non-sensitive accuracy data.

12 . The method of claim 11 , further comprising operating the comparator component to deliver the non-sensitive accuracy data to the open model-based machine learning engine.

13 . A computer program product stored on a non-transitory computer-readable medium and comprising computer program code to, when loaded into a computer system and executed thereon, cause the computer system to:

receive a data request from an open model-based machine learning engine to initiate data capture;

responsive to receiving the data request, capture data comprising sensitive and non-sensitive data to a first dataset;

scan the first dataset to detect the sensitive data;

responsive to detecting the sensitive data, create an obscured representation of the sensitive data to be stored with the non-sensitive data in a second dataset; and

deliver the second dataset to the open model-based machine learning engine.