Privacy Preserving Conversion Modeling in Data Clean Room

TL;DR

This paper introduces a privacy-preserving framework for conversion rate prediction in online advertising, enabling collaborative model training in data clean rooms without sharing sensitive data, while maintaining high accuracy and reducing communication costs.

Contribution

The paper presents a novel training framework that uses batch-level gradients, adapter-based fine-tuning, and de-biasing techniques to ensure privacy and efficiency in CVR prediction models.

Findings

Achieves competitive ROCAUC performance on industrial datasets.

Reduces communication overhead significantly.

Maintains privacy compliance for users and advertisers.

Abstract

In the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the challenge of CVR prediction while adhering to user privacy preferences and advertiser requirements. Traditional methods face obstacles such as the reluctance of advertisers to share sensitive conversion data and the limitations of model training in secure environments like data clean rooms. We propose a novel model training framework that enables collaborative model training without sharing sample-level gradients with the advertising platform. Our approach introduces several innovative components: (1) utilizing batch-level aggregated gradients instead of sample-level gradients to minimize privacy risks; (2) applying adapter-based parameter-efficient fine-tuning and gradient compression to reduce communication costs; and (3) employing de-biasing techniques to train the model under label differential privacy, thereby maintaining accuracy despite privacy-enhanced label perturbations. Our experimental results, conducted on industrial datasets, demonstrate that our method achieves competitive ROCAUC performance while significantly decreasing communication overhead and complying with both advertiser privacy requirements and user privacy choices. This framework establishes a new standard for privacy-preserving, high-performance CVR prediction in the digital advertising landscape.