PrivFusionNet: Mitigating Identity Re-Leakage in EEG–Eye Movement Fusion for Emotion Recognition

Multimodal fusion leverages complementary signals but can also recombine residual subject-specific cues, causing identity information suppressed within individual modalities to re-emerge after fusion. Existing privacy-preserving approaches primarily operate at the unimodal level, leaving fusion-level privacy underexplored. Using emotion recognition from EEG and eye-movement signals as a testbed, we formulate fusion-induced identity re-leakage and show that unimodal identity suppression alone does not reliably protect fused representations. We propose PrivFusionNet, a privacy-preserving multimodal representation learning framework that directly protects the fusion space through three complementary mechanisms. Privacy-aware low-rank fusion reweights cross-modal interaction components according to task utility and identity risk; fusion-level task-identity disentanglement attenuates identity-related directions while retaining emotion-discriminative information; and a fused privacy codebook quantizes continuous representations into subject-shared task prototypes to suppress fine-grained identity cues. Under leave-one-session-out privacy evaluation, PrivFusionNet reduces fused-representation user identification accuracy from 25.33% to 7.57% on SEED-IV and from 46.38% to 6.11% on SEED-V. Under leave-one-subject-out emotion-recognition evaluation, it achieves mean accuracies of 84.23% and 88.30%, respectively. These results identify fusion-induced identity re-leakage as a practical privacy risk and demonstrate a favorable privacy-utility trade-off.