Abstract: The present disclosure provides a secure three-party multiplication method and system for privacy computing, involving the technical field of privacy computing. The method includes that an auxiliary compute node generates three groups of random matrix pairs randomly and transmits the random matrix pairs to three parties, and the three parties compute respective sum matrixes based on a sum of respective random matrixes and private matrixes, respectively Â, ? and {circumflex over (B)}. A second party generates a matrix set according to a sum matrix, a first party obtains Ta based on the matrix set and its own secret matrix, the second party obtains Tb based on its own random secret matrix and Ta, and the third party generates its own random secret matrix based on Tb and the matrix set, and obtains a product matrix according to three random secret matrixes. The present disclosure can improve reliability of result accuracy.

Abstract: The present disclosure provides an end-to-end efficient privacy-preserving computation apparatus and method for secure two-party matrix inversion, relating to the technical field of privacy-preserving computation. In the present disclosure, the respective corresponding output matrices are determined using the privacy-preserving computation request and the private data matrices and then sent to the requesting party of the secure two-party inversion computation, so that the requesting party obtains the final inversion computation result. This solves the problems of large computation and communication overhead in ciphertext space caused by the introduction of homomorphic encryption and oblivious transfer techniques in the prior art, as well as the privacy and security issues caused by the leakage of original data and the loss of precision in floating-point number calculation due to the limitation of fixed-length digits in ciphertext computation.

Abstract: The present disclosure provides an end-to-end efficient privacy-preserving computation apparatus and method for secure two-party matrix inversion, relating to the technical field of privacy-preserving computation. In the present disclosure, the respective corresponding output matrices are determined using the privacy-preserving computation request and the private data matrices and then sent to the requesting party of the secure two-party inversion computation, so that the requesting party obtains the final inversion computation result. This solves the problems of large computation and communication overhead in ciphertext space caused by the introduction of homomorphic encryption and oblivious transfer techniques in the prior art, as well as the privacy and security issues caused by the leakage of original data and the loss of precision in floating-point number calculation due to the limitation of fixed-length digits in ciphertext computation.

Abstract: The present disclosure provides an anti-malicious method, device and medium for secure three-party computation, and relates to the field of data security. The method includes the following: Respective private data matrices of three participants are determined; each participant receives a corresponding random matrix pair generated by a commodity server node; and based on the random matrix pair, a corresponding internal matrix is generated in a computational process of the three participants, and corresponding security constraints are separately added to a computational process in which a collusion behavior exists and no collusion behavior exists. The security constraints implement a constraint on a rank of an internal matrix, so that any participant in the computational process cannot predict private data matrices of another two participants. The present disclosure can improve data security of the secure three-party computation.

Abstract: The present disclosure provides an anti-malicious method, device and medium for secure three-party computation, and relates to the field of data security. The method includes the following: Respective private data matrices of three participants are determined; each participant receives a corresponding random matrix pair generated by a commodity server node; and based on the random matrix pair, a corresponding internal matrix is generated in a computational process of the three participants, and corresponding security constraints are separately added to a computational process in which a collusion behavior exists and no collusion behavior exists. The security constraints implement a constraint on a rank of an internal matrix, so that any participant in the computational process cannot predict private data matrices of another two participants. The present disclosure can improve data security of the secure three-party computation.

Abstract: The present disclosure provides a secure three-party multiplication method and system for privacy computing, involving the technical field of privacy computing. The method includes that an auxiliary compute node generates three groups of random matrix pairs randomly and transmits the random matrix pairs to three parties, and the three parties compute respective sum matrixes based on a sum of respective random matrixes and private matrixes, respectively Â, ? and {circumflex over (B)}. A second party generates a matrix set according to a sum matrix, a first party obtains Ta based on the matrix set and its own secret matrix, the second party obtains Tb based on its own random secret matrix and Ta, and the third party generates its own random secret matrix based on Tb and the matrix set, and obtains a product matrix according to three random secret matrixes. The present disclosure can improve reliability of result accuracy.