Use of Differential Privacy Techniques to Measure Incrementality of Ad Performance on Digital Platforms without Exchange of PII

Authors: Varun Chivukula

DOI: https://doi.org/10.5281/zenodo.14382601

Short DOI: https://doi.org/g8vbgb

Country: USA

Full-text Research PDF File:   View   |   Download

Abstract: Incrementality measurement is critical for evaluating the causal impact of digital ad campaigns. Typically, these analyses rely on precise user-level data for randomized control trials (RCTs), often necessitating the exchange of Personally Identifiable Information (PII) between ad platforms and advertisers. Differential Privacy (DP) offers a robust solution to this challenge by introducing noise into the data, thereby ensuring privacy without the need for PII exchange. This paper presents a detailed methodology for applying DP to incrementality measurement in digital advertising. We formulate the problem mathematically, outline a framework for incorporating DP mechanisms, and explore practical considerations such as privacy budget management, noise scaling, and the balance between privacy and utility. We also provide an in-depth simulation study to quantify the effectiveness of DP in protecting user privacy while maintaining accurate causal lift estimation.

Keywords: Privacy enhancing technologies (PETs), Causal inference, Randomized control Trials, Differential Privacy

Paper Id: 231822

Published On: 2021-07-06

Published In: Volume 9, Issue 4, July-August 2021

Cite This: Use of Differential Privacy Techniques to Measure Incrementality of Ad Performance on Digital Platforms without Exchange of PII - Varun Chivukula - IJIRMPS Volume 9, Issue 4, July-August 2021. DOI 10.5281/zenodo.14382601

Share this