The invention discloses a
big data privacy protection modeling method and
system based on
federated learning and a block chain, and relates to the technical field of
privacy protection and joint modeling. According to the method,
homomorphic encryption,
differential privacy,
federated learning, secure multi-party computing and block chain technologies are fused,
big data privacy protection and joint modeling are realized,
encryption and
dimensionality reduction are performed on
original data through
homomorphic encryption and
differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted
data set through
federated learning, and a
big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a
verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding
noise optimization performance for a
global model by using
differential privacy, testing generalization ability through
cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance.
Big data values are released, and data protection regulations and privacy requirements are met.