Machine learning based on homomorphic encryption

Patent metadata

Field	Value
Publication number	US-11551035-B2
Application number	US-201816055705-A
Country	US
Kind code	B2
Filing date	Aug 6, 2018
Priority date	Feb 4, 2016
Publication date	Jan 10, 2023
Grant date	Jan 10, 2023

Abstract

A method for evaluating data is based on a computational model, the computational model comprising model data, a training function and a prediction function. The method includes training the computational model by: receiving training data and training result data for training the computational model, and computing the model data from the training data and the training result data with the training function. The method includes predicting result data by: receiving field data for predicting result data; and computing the result data from the field data and the model data with the prediction function. The training data may be plaintext and the training result data may be encrypted with a homomorphic encryption algorithm, wherein the model data may be computed in encrypted form from the training data and the encrypted training result data with the training function. The field data may be plaintext, wherein the result data may be computed in encrypted form from the field data and the encrypted model data with the prediction function.

First claim

The invention claimed is:

1. A method for evaluating data based on a computational model, the computational model comprising model data, a training function and a prediction function, the method comprising:
training the computational model by:
receiving training data and training result data for training the computational model;
computing the model data from the training data and the training result data with the training function;
predicting result data by:
receiving field data for predicting result data; and
computing the result data from the field data and the model data with the prediction function;
wherein,
the training data is plaintext and the training result data is encrypted with a homomorphic encryption algorithm, the homomorphic encryption algorithm being additively homomorphic;
the model data is computed in encrypted form from the plaintext training data and the encrypted training result data with the training function;
the field data is plaintext, wherein the result data is computed in encrypted form from the plaintext field data and the encrypted model data with the prediction function, wherein two or less data types are encrypted at any given time, the datatypes including the training data, the model data, and the field data; and
wherein, the computational model is a linear regression model, in which the prediction function is a linear function in the field data and the model data;
wherein, the training function of the linear regression model is based on minimizing a cost function, which quadratically minimizes a difference between the prediction function and the training result data.

2. The method of claim 1 , wherein the training function is a polynomial in at least one of the training data and the training result data; and wherein the prediction function is a polynomial in at least one of the field data and model data.

3. The method of claim 1 , wherein each of the training data and the field data is a set of vectors or matrices of field data values; wherein each of the training result data and the result data is a vector of result data values; wherein the model data is a vector of model data values.

4. The method of claim 1 , wherein, before encryption, data values are approximated by multiplication with an approximation factor and rounding to a closest integer; and wherein the homomorphic encryption algorithm is based on a finite field based on the product of two prime numbers and the approximation factor is smaller than this product.

5. The method of claim 1 , wherein the training function for computing the model data is iteratively updated with the training data and the training result data; wherein in each iteration, the training function is evaluated in an evaluation server device, the updated model data is sent to a secure device, decrypted, multiplied with a convergence factor and encrypted in the secure device and sent back to the evaluation server device.

6. The method of claim 1 , wherein at least one of the training data and the training result data is provided by a client device communicatively interconnected with an evaluation server device, wherein the client device encrypts at least one of the training data and the training result data and decrypts the result data and wherein the evaluation server device at least partially computes at least one of the model data and the result data; and wherein the field data is provided by at least one or a plurality of devices communicatively interconnected with the evaluation server device.

7. The method of claim 1 , wherein the training data and the field data encodes at least one of local wind speed and local sunshine intensity and the training result data and the result data encodes at least one of electrical power production of wind turbine facilities and solar energy facilities.

8. A method for evaluating data based on a computational model, the computational model comprising model data, a training function and a prediction function, the method comprising:
training the computational model by:
receiving training data and training result data for training the computational model;
computing the model data from the training data and the training result data with the training function;
predicting result data by:
receiving field data for predicting result data; and
computing the result data from the field data and the model data with the prediction function;
wherein,
the training data and the training result data are encrypted with a homomorphic encryption algorithm, the homomorphic encryption algorithm being additively homomorphic;
the model data is computed in plaintext from encrypted training data and the encrypted training result data with the training function;
the field data is encrypted with homomorphic encryption algorithm, wherein the result data is computed in encrypted form from the encrypted field data and the plaintext model data with the prediction function, wherein two or less data types are encrypted at any given time, the data types including the training data, the model data, and the field data; and
wherein, the computational model is a linear regression model, in which the prediction function is a linear function in the field data and the model data;
wherein, the training function of the linear regression model is based on minimizing a cost function, which quadratically minimizes a difference between the prediction function and the training result data.

9. The method of claim 8 , wherein the training function is a polynomial in at least one of the training data and the training result data; and wherein the prediction function is a polynomial in at least one of the field data and model data.

10. The method of claim 8 , wherein each of the training data and the field data is a set of vectors or matrices of field data values, wherein each of the training result data and the result data is a vector of result data values; wherein the model data is a vector of model data values.

11. The method of claim 8 , wherein, before encryption, data values are approximated by multiplication with an approximation factor and rounding to a closest integer; and wherein the homomorphic encryption algorithm is based on a finite field based on the product of two prime numbers and the approximation factor is smaller than this product.

12. The method of claim 8 , wherein the training function for computing the model data is iteratively updated with the training data and the training result data; wherein in each iteration, the training function is evaluated in an evaluation server device, the updated model data is sent to a secure device, decrypted, multiplied with a convergence factor and encrypted in the secure device and sent back to the evaluation server device.

13. The method of claim 8 , wherein at least one of the training data and the training result data is provided by a client device communicatively interconnected with an evaluation server device, wherein the client device encrypts at least one of the training data and the training result data and decrypts the result data and wherein the evaluation server device at least partially computes at least one of the model data and the result data; and wherein the field data is provided by at least one or a plurality of devices communicatively interconnected with the evaluation server device.

14. A computer-readable medium executing a computer program for evaluating data based on a computational model, the computational model comprising model data, a training function and a prediction function, which, when executed on an evaluation system, comprises:
training the computational model by:
receiving trai

Assignees

• Abb Schweiz Ag

Inventors

• Schneider Johannes
• Harvan Matus
• Obermeier Sebastian
• Locher Thomas
• Pignolet Yvonne-Anne

Classifications

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• Y04S40/20

  Information technology specific aspects, e.g. CAD, simulation, modelling, system security · CPC title

• H04L2209/76

  Proxy, i.e. using intermediary entity to perform cryptographic operations · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• H04L9/008

  involving homomorphic encryption · CPC title