Medical privacy data protection method based on federated learning tensor factorization

• Main Authors: MAI CHENGYUAN, ZHENG ZIBIN, CHEN CHUAN
• Format: Patent
• Language: Chinese; English
• Published: 15.06.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

The invention discloses a medical privacy data protection method based on federated learning tensor factorization, and the method comprises the following specific steps: 1, each medical institution needs to maintain a locally decomposed tensor factor matrix and a global tensor non-patient factor matrix, initialization is carried out when the federation process is started; 2, each medical institution is enabled to perform local tensor factorization training, and gradient descent by using a loss function; 3, calculating of a corresponding factor matrix updating gradient according to the locally decomposed factor matrix and the global non-patient factor matrix is carried out; according to the medical privacy data protection method based on federated learning tensor factorization, the user data privacy can be further protected while the communication efficiency is improved, meanwhile, the calculation amount of homomorphic encryption is reduced, and the problem that the accuracy of an aggregated global factor matr