Use of Differential Privacy to Enable Optimization of Ads on Ad Platforms without Exchange of Personally Identifiable Information (Pii)
Author(s): Varun Chivukula
Publication #: 2412120
Date of Publication: 08.10.2022
Country: USA
Pages: 1-4
Published In: Volume 8 Issue 5 October-2022
DOI: https://doi.org/10.5281/zenodo.14566262
Abstract
Digital advertising platforms rely on machine learning (ML) models to optimize ad targeting, maximize conversions, and improve overall campaign performance. Traditionally, these models are trained on centralized datasets containing Personally Identifiable Information (PII), raising significant privacy concerns. With the advent of privacy regulations such as GDPR and CCPA, there is an urgent need for solutions that ensure data privacy while maintaining model performance. Differential Privacy (DP) offers a robust framework for training ML models by adding controlled noise to data, ensuring that individual user information remains confidential without sacrificing analytical insights.
This paper explores the integration of DP into ad delivery platforms, focusing on how it enables the development of conversion-optimized ML models without exposing PII. We provide an overview of DP principles, practical applications in digital advertising, and examples of its effectiveness. The paper also addresses implementation challenges, such as trade-offs between privacy and accuracy, and highlights future directions for leveraging DP in real-time, large-scale advertising environments.
Keywords: Differential Privacy, Machine Learning, Privacy-Preserving Advertising, Ad Delivery Platforms, Conversion Optimization, Data Privacy, Digital Marketing
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