Use of Multiparty Computation for Ad Optimization without Exchange of User PII Data

Authors: Varun Chivukula

DOI: https://doi.org/10.5281/zenodo.14382656

Short DOI: https://doi.org/g8vbg8

Country: USA

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Abstract: The optimization of ad performance on digital platforms is a cornerstone of modern advertising, requiring efficient data processing over massive datasets. However, privacy concerns prevent the sharing of personally identifiable information (PII) between entities. To address this, we explore the application of Multiparty Computation (MPC) for privacy-preserving optimization. This framework allows parties to jointly compute ad targeting and bidding strategies without disclosing individual-level user data. We formalize the problem, propose a protocol for secure computation, and provide simulations to evaluate the effectiveness of the approach in ensuring privacy while optimizing advertising outcomes.

Keywords: Privacy enhancing technologies, Digital Ad platforms, Auction based RTB’s, Data encryption and retrieval, Randomized control trials

Paper Id: 231823

Published On: 2021-09-06

Published In: Volume 9, Issue 5, September-October 2021

Cite This: Use of Multiparty Computation for Ad Optimization without Exchange of User PII Data - Varun Chivukula - IJIRMPS Volume 9, Issue 5, September-October 2021. DOI 10.5281/zenodo.14382656

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