The application relates to the technical field of privacy data protection, and discloses a
ubiquitous network multi-
modal privacy data protection method, which comprises the following steps: a
client performs band pass filtering,
standardization and sliding window
slicing pretreatment on multi-
modal physiological signals such as electroencephalogram, electrooculogram and electromyogram;
frequency band discriminant features are extracted and fused from the obtained
time sequence data window according to a preset
frequency band; model training is performed locally, and a parameter update difference value is calculated, and
differential privacy noise is injected; a non-interactive zero-knowledge proof is generated based on the Groth16 protocol, and the noisy difference value and the proof are uploaded to a
trusted third party; after
verification by the
trusted third party, the difference value is confused and forwarded to a central
server; and the central
server aggregates and updates a
global model according to sample weights and distributes the
global model to the
client. The method realizes efficient cooperation of multi-
modal signal end-side adaptive fusion and distributed
privacy protection, and guarantees the safety and credibility of parameter interaction through a non-interactive proof mechanism.