Distributed multi-party security model training framework for privacy protection
AssigneeAdvanced New Technologies Co., Ltd.
InventorsHuazhong Wang, Shan Yin, Pengfei Ying
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Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a multi-party secure logistic regression model (SLRM). One of the methods includes receiving, at a plurality of secure computation nodes (SCNs), a plurality of random numbers from a random number provider; encrypting, at each SCN, data stored at the SCN using the received random numbers; iteratively updating a secure logistic regression model (SLRM) by using the encrypted data from each SCN; 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.
Claims20
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;
- iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first 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.
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;
- iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first 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.
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;
- iteratively updating, by the first SCN, a secure logistic regression model (SLRM) by using the first 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.
Dependencies are inferred from the claim text.