Distributed multi-party security model training framework for privacy protection

1. A computer-implemented method for training multi-party secure logistic regression models, the computer-implemented method comprising:

receiving, by a first secure computation node (SCN) of a plurality of secure computation nodes (SCNs), a first random number of a plurality of random numbers from a random number provider;

encrypting, by the first SCN, data stored at the SCN using the first random number to generate a first encrypted data;

transmitting, by the first SCN, the first encrypted data to a second SCN of the plurality of SCNs;

receiving, by the first SCN and from the second SCN, a second encrypted data that was encrypted using a second random number of the plurality of random numbers from the random number provider;

iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first encrypted data and the second encrypted data to generate an interim computation result that is divided into a plurality of shares that are transmitted to the plurality of SCNs to be used as subsequent inputs for the SLRM to determine a column vector of a prediction error as a difference between a sigmoid fitting function applied to a respective share and a label of a sample of a mini batch of a Stochastic Gradient Descent method, each of the plurality of shares hiding original data such that the original data cannot be recovered by using a single share of the plurality of shares; and

after iteratively updating the SLRM, outputting a result of the SLRM, wherein the result is configured to enable a service to be performed by each SCN of the plurality of SCNs.

2. The computer-implemented method of claim 1 , wherein each random number is at least one of a number, a vector, or a matrix.

3. The computer-implemented method of claim 1 , wherein at least one of the random numbers is generated by a third-party agent.

4. The computer-implemented method of claim 1 , wherein iteratively updating the SLRM is based on a Secret Sharing (SS) scheme.

5. The computer-implemented method of claim 1 , wherein iteratively updating the SLRM comprises:

receiving a notification that a portion of a service process is completed; and

in response to receiving the notification, initiating a second portion of the service process.

6. The computer-implemented method of claim 1 , further comprising:

prior to receiving a plurality of random numbers from the random number provider, initializing parameters associated with the SLRM.

7. The computer-implemented method of claim 1 , wherein iteratively updating the SLRM continues until a difference between two consecutive iteration results is less than a predetermined threshold.

8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

receiving, by a first secure computation node (SCN) of a plurality of secure computation nodes (SCNs), a first random number of a plurality of random numbers from a random number provider;

encrypting, by the first SCN, data stored at the SCN using the first random number to generate a first encrypted data;

transmitting, by the first SCN, the first encrypted data to a second SCN of the plurality of SCNs;

receiving, by the first SCN and from the second SCN, a second encrypted data that was encrypted using a second random number of the plurality of random numbers from the random number provider;

iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first encrypted data and the second encrypted data to generate an interim computation result that is divided into a plurality of shares that are transmitted to the plurality of SCNs to be used as subsequent inputs for the SLRM to determine a column vector of a prediction error as a difference between a sigmoid fitting function applied to a respective share and a label of a sample of a mini batch of a Stochastic Gradient Descent method, each of the plurality of shares hiding original data such that the original data cannot be recovered by using a single share of the plurality of shares; and

after iteratively updating the SLRM, outputting a result of the SLRM, wherein the result is configured to enable a service to be performed by each SCN of the plurality of SCNs.

9. The non-transitory, computer-readable medium of claim 8 , wherein each random number is at least one of a number, a vector, or a matrix.

10. The non-transitory, computer-readable medium of claim 8 , wherein at least one of the random numbers is generated by a third-party agent.

11. The non-transitory, computer-readable medium of claim 8 , wherein iteratively updating the SLRM is based on a Secret Sharing (SS) scheme.

12. The non-transitory, computer-readable medium of claim 8 , wherein iteratively updating the SLRM comprises:

receiving a notification that a portion of a service process is completed; and

in response to receiving the notification, initiating a second portion of the service process.

13. The non-transitory, computer-readable medium of claim 8 , the operations comprising:

prior to receiving the plurality of random numbers from the random number provider, initializing parameters associated with the SLRM.

14. The non-transitory, computer-readable medium of claim 8 , wherein iteratively updating the SLRM continues until a difference between two consecutive iteration results is less than a predetermined threshold.

15. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:

receiving, receiving, by a first secure computation node (SCN) of a plurality of secure computation nodes (SCNs), a first random number of a plurality of random numbers from a random number provider;

encrypting, by the first SCN, data stored at the SCN using the first random number to generate a first encrypted data;

transmitting, by the first SCN, the first encrypted data to a second SCN of the plurality of SCNs;

receiving, by the first SCN and from the second SCN, a second encrypted data that was encrypted using a second random number of the plurality of random numbers from the random number provider;

iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first encrypted data and the second encrypted data to generate an interim computation result that is divided into a plurality of shares that are transmitted to the plurality of SCNs to be used as subsequent inputs for the SLRM to determine a column vector of a prediction error as a difference between a sigmoid fitting function applied to a respective share and a label of a sample of a mini batch of a Stochastic Gradient Descent method, each of the plurality of shares hiding original data such that the original data cannot be recovered by using a single share of the plurality of shares; and

after iteratively updating the SLRM, outputting a result of the SLRM, wherein the result is configured to enable a service to be performed by each SCN of the plurality of SCNs.

16. The computer-implemented system of claim 15 , wherein each random number is at least one of a number, a vector, or a matrix.

17. The computer-implemented system of claim 15 , wherein at least one of the random numbers is generated by a third-party agent.

18. The computer-implemented system of claim 15 , wherein iteratively updating the SLRM is based on a Secret Sharing (SS) scheme.