Privacy preserving ensemble learning as a service

Patent metadata

Field	Value
Publication number	US-12169557-B2
Application number	US-202117351569-A
Country	US
Kind code	B2
Filing date	Jun 18, 2021
Priority date	Jun 18, 2021
Publication date	Dec 17, 2024
Grant date	Dec 17, 2024

Abstract

Techniques described herein relate to a method for predicting results using ensemble models. The method may include receiving trained model data sets from a model source nodes, each trained model data set comprising a trained model, an important feature list, and a missing feature generator; receiving a prediction request data set; making a determination that the prediction request data set does not include an input feature for a trained model; generating, based on the determination and using a missing feature generator, a substitute feature to replace the input feature; executing the trained model using the prediction request data set and the substitute feature to obtain a first prediction; executing a second trained model using the prediction request data set to obtain a second prediction; and obtaining a final prediction using the first prediction, the second prediction, and an ensemble model.

First claim

What is claimed is:

1. A method for predicting results using ensemble models, the method comprising:
receiving a first trained model data set from a first model source node, the first trained model data set comprising:
a first trained model,
a first critical feature list comprising critical properties of the first trained model data set, wherein the critical properties of the first training model data set are inputs for the first trained model relevant to a result produced by the first trained model, and
a first missing feature generator configured to generate missing critical properties of the first trained model data set based on the first critical feature list;

receiving a second trained model data set from a second model source node, the second trained model data set comprising:
a second trained model,
a second critical feature list comprising critical properties of the second trained model data set, wherein the critical properties of the second trained model data set are inputs for the second trained model relevant to a result produced by the second trained model, and
a second missing feature generator, configured to generate missing critical properties of the second trained model data set based on the second critical feature list;

receiving a prediction request data set comprising a third critical feature list, the third critical feature list comprising critical properties of the prediction request data set, wherein the critical properties of the prediction request data set are properties to be compared to the first critical feature list and the second critical feature list;
making a first determination that the third critical feature list is missing at least one critical feature from the first critical feature list;
generating, based on the first determination and using the first missing feature generator and using a General Adversarial Network (GAN), a first substitute feature to replace the at least one missing critical feature, to obtain a modified prediction request data set;
executing the first trained model using the modified prediction request data set to obtain a first prediction;
executing the second trained model using the prediction request data set to obtain a second prediction; and
obtaining a final prediction using the first prediction and the second prediction as inputs to an ensemble model.

2. The method of claim 1 , further comprising, before receiving the prediction request data set, training the ensemble model using an ensemble model training set and a plurality of missing feature values generated using a plurality of missing feature generators.

3. The method of claim 1 , wherein the first missing feature generator is a GAN.

4. The method of claim 1 , further comprising, before generating the first substitute feature, making a second determination that the at least one missing critical feature is not included in the first critical feature list, wherein generating the first substitute feature is further based on the second determination.

5. The method of claim 1 , wherein the prediction request data set is received from an entity seeking a prediction related to a potential cyber-attack.

6. The method of claim 1 , wherein:
the first trained model data set is received from a first participant entity in a collaborative ensemble modeling service,
the second trained model data set is received from a second participant entity in a collaborative ensemble modeling service, and
no data used to train the first trained model and the second trained model is shared between the first participant entity and the second participant entity.

7. The method of claim 1 , wherein the final prediction is obtained, at least in part, using a weighted average of the first prediction and the second prediction.

8. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for predicting results using ensemble models, the method comprising:
receiving a first trained model data set from a first model source node, the first trained model data set comprising:
a first trained model,
a first critical feature list comprising critical properties of the first trained model data set, wherein the critical properties of the first training model data set are inputs for the first trained model relevant to a result produced by the first trained model, and
a first missing feature generator configured to generate missing critical properties of the first trained model data set based on the first critical feature list;

receiving a second trained model data set from a second model source node, the second trained model data set comprising:
a second trained model,
a second critical feature list comprising critical properties of the second trained model data set, wherein the critical properties of the second trained model data set are inputs for the second trained model relevant to a result produced by the second trained model, and
a second missing feature generator, configured to generate missing critical properties of the second trained model data set based on the second critical feature list;

receiving a prediction request data set comprising a third critical feature list, the third critical feature list, comprising critical properties of the prediction request data set, wherein the critical properties of the prediction request data set are properties to be compared to the first critical feature list and the second critical feature list;
making a first determination that the third critical feature list is missing at least one critical feature from the first critical feature list;
generating, based on the first determination and using the first missing feature generator and using a General Adversarial Network (GAN), a first substitute feature to replace the at least one missing critical feature, to obtain a modified prediction request data set;
executing the first trained model using the modified prediction request data set to obtain a first prediction;
executing the second trained model using the prediction request data set to obtain a second prediction; and
obtaining a final prediction using the first prediction and the second prediction as inputs to an ensemble model.

9. The non-transitory computer readable medium of claim 8 , wherein the method performed by executing the computer readable program code further comprises, before receiving the prediction request data set, training the ensemble model using an ensemble model training set and a plurality of missing feature values generated using a plurality of missing feature generators.

10. The non-transitory computer readable medium of claim 8 , wherein the first missing feature generator is a GAN.

11. The non-transitory computer readable medium of claim 8 , wherein the method performed by executing the computer readable program code further comprises, before generating the first substitute feature, making a second determination that the at least one missing critical feature is not included in the first critical feature list, wherein generating the first substitute feature is further based on the second determination.

12. The non-transitory computer readable medium of claim 8 , wherein the prediction request data set is received from an entity seeking a prediction related to a potential cyber-attack.

13. The non-transitory computer readable medium of claim 8 , wherein:
the first trained model data set is received from a first participant entity in a collaborative ensemble modeling service,
the second trained model data set is received from a second participant entity in a c

Assignees

• Emc Ip Holding Co Llc

Inventors

• Gaber Shiri
• Arnon Ohad
• Shapiro Dany

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title