Privacy Preserving Conversion Modeling in Data Clean Room
In data clean room settings, CVR prediction faces dual constraints: stringent user privacy protection and the requirement that advertisers’ data remain within their own domain. To address this, we propose the first collaborative training framework integrating batch-level gradient aggregation, Adapter-based efficient fine-tuning, and label differential privacy with bias mitigation. Without sharing raw labels or model parameters, our method enables cross-domain joint modeling via gradient-level collaboration: batch-wise gradient aggregation ensures regulatory compliance; lightweight Adapters enable low-overhead domain adaptation; and bias-corrected label differential privacy mitigates estimation bias induced by noise injection. Evaluated on industrial datasets, our approach achieves state-of-the-art ROC-AUC performance while reducing communication overhead by 62%. It strictly adheres to GDPR and other privacy regulations, fulfilling practical commercial deployment requirements.