Privacy Preserving Conversion Modeling in Data Clean Room

• URL: http://arxiv.org/abs/2505.14959v1
• Date: Tue, 20 May 2025 22:38:50 GMT
• Title: Privacy Preserving Conversion Modeling in Data Clean Room
• Authors: Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu, Jiankai Sun, Behnam Rezaei,
• Abstract summary: This paper addresses the challenge of CVR prediction while adhering to user privacy preferences and advertiser requirements.<n>Traditional methods face obstacles such as the reluctance of advertisers to share sensitive conversion data.<n>We propose a novel model training framework that enables collaborative model training without sharing sample-level gradients with the advertising platform.
• Score: 9.75348287904258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Improving Noise Efficiency in Privacy-preserving Dataset Distillation [59.57846442477106]
  We introduce a novel framework that decouples sampling from optimization for better convergence and improves signal quality.<n>On CIFAR-10, our method achieves a textbf10.0% improvement with 50 images per class and textbf8.3% increase with just textbfone-fifth the distilled set size of previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-03T13:15:52Z)
• Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy [6.517767018830131]
  We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce privacy budget waste.<n>We also introduce GDPFed$+$, which integrates model sparsification to eliminate unnecessary noise and optimize per-group client sampling ratios.
  arXiv  Detail & Related papers  (2025-05-19T18:55:34Z)
• Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning [12.278668095136098]
  Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data.<n>We propose an adaptive clipping mechanism that dynamically adjusts the clipping norm using a multi-objective optimization framework.<n>Our results show that adaptive clipping consistently outperforms fixed-clipping baselines, achieving improved accuracy under the same privacy constraints.
  arXiv  Detail & Related papers  (2025-03-27T04:57:05Z)
• Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation [1.519321208145928]
  Federated Learning (FL) offers a promising framework for individuals aiming to collaboratively develop a shared model.<n> variations in data distribution among clients can profoundly affect FL methodologies, primarily due to instabilities in the aggregation process.<n>We propose a novel FL framework, combining individual parameter pruning and regularization techniques to improve the robustness of individual clients' models to aggregate.
  arXiv  Detail & Related papers  (2024-12-19T16:22:37Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage [0.8029049649310213]
  Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. gradient perturbation techniques have been proposed to enhance privacy, but they come at the cost of reduced model performance, increased convergence time, or increased data demand. We introduce PRECODE, a PRivacy EnhanCing mODulE that can be used as generic extension for arbitrary model architectures.
  arXiv  Detail & Related papers  (2021-08-10T14:43:17Z)
• DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation [15.63770709526671]
  We propose a scalable privacy-preserving generative model DATALENS. We show that, DATALENS significantly outperforms other baseline DP generative models. We adapt the proposed TOPAGG approach, which is one of the key building blocks in DATALENS, to DP SGD training.
  arXiv  Detail & Related papers  (2021-03-20T06:14:19Z)
• A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework [56.57225686288006]
  Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
  arXiv  Detail & Related papers  (2020-03-13T23:52:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.