Longitudinal federated learning-based social network cross-platform malicious user detection method

• Main Authors: NIU DANMEI, ZHANG XIAOGUO, ZHANG ZHIYONG, LI YUXIANG, ZHANG LILI, ZHANG LANFANG, LIANG TENGXIANG, WEI XINLE, XIANG FEI, SONG BIN
• Format: Patent
• Language: Chinese; English
• Published: 29.06.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a social network cross-platform malicious user detection method based on longitudinal federated learning. The method comprises the following steps: step 1, constructing a social network cross-platform malicious user detection hierarchical architecture based on longitudinal federated learning; 2, dividing participants into active parties and passive parties, and performing preprocessing operation on sample data of the active parties and the passive parties in a data preprocessing layer to obtain structured data; step 3, mapping common sample data of the active party and the passive party by the structured data processed by the data preprocessing layer; 4, cooperatively training a global model under the definition of machine learning, and encrypting and decrypting data of an active party and a passive party by using homomorphic encryption to complete federal learning layer training; step 5, enabling the active party and the passive party to update own local model training parameters and