Abstract: A server-assisted secure two-party inversion computation method, system, and apparatus are disclosed, relating to the field of data security. The method includes the following steps: the participant node A, the participant node Bob, the auxiliary computing node P, the auxiliary computing node Q, and the auxiliary computing node S perform a third three-party matrix multiplication based on a secure three-party matrix multiplication protocol 3PMP, and a result of the three-party matrix multiplication is randomly split into private matrices, which are then sent to a party requesting the secure two-party inversion computation, to obtain a final result of the two-party inversion computation. The present disclosure has characteristics such as parallelism, security, reliability, and the ability to effectively resist collusion among a plurality of server nodes.

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 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 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: Disclosed is an intelligent control system of spunlace production line, which includes a data acquiring module, which is used for acquiring and storing real-time production line data; the production line data includes cotton feeding roller value, real-time moisture value, real-time speed value and real-time gram weight value; the data process module is used for classify and controlling that production line data, and giving the adjustment opinions of the cotton feeding roller parameters; the parameter control module is used for verifying the parameter adjustment opinions and applying the opinions to the control system; the data acquiring module, the data processing module and the parameter control module are connected in sequence.