Impact of GDPR Compliance on Advertising Recommendation Systems: Algorithmic Challenges, Privacy-Preserving Solutions, and Performance Trade Offs

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Srinivasan Ayyangar

10.14445/23488387/IJCSE-V12I8P105

How to Cite?

Srinivasan Ayyangar, "Impact of GDPR Compliance on Advertising Recommendation Systems: Algorithmic Challenges, Privacy-Preserving Solutions, and Performance Trade Offs," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 8, pp. 31-38, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I8P105

Abstract:

Europe’s General Data Protection Regulation (GDPR) disrupted traditional advertising recommendation architectures based on unrestricted behavioral profiling towards privacy-centric algorithms. This study aims to examine how the GDPR’s restrictions on personal data usage catalyzed algorithmic breakthroughs in ad algorithms — differential privacy implementation, federated learning architectures, and contextual targeting mechanisms. Empirical findings demonstrate that while regulatory compliance initially imposed measurable efficiency costs—including a 2.1% reduction in click-through rate and 5.4% decrease in conversion rate — subsequent technological adaptations proved remarkably resilient. Privacy-preserving methodologies enabled an ecosystem to move towards architectures balancing user data protection with commercial sustainability and accuracy. This research paper aims to establish a foundational framework for technologists, academics, and practitioners addressing the convergence of privacy regulation and recommendation system engineering in current advertising environments.

Keywords:

General Data Protection Regulation, Privacy-Preserving Machine Learning, Advertising Recommendation Systems, Differential Privacy, Federated Learning, Contextual Targeting.

References:

[1] European Parliament and Council, “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and On the Free Movement of Such Data and Repealing Directive 95/46/EC,” Official Journal of the European Union, 2016.
[Google Scholar] [Publisher Link]
[2] Avi Goldfarb and Catherine E. Tucker, “Privacy Regulation and Online Advertising,” Management Science, vol. 57, no. 1, pp. 57-71, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bryce Goodman and Seth Flaxman, “European Union Regulations on Algorithmic Decision-Making and a ‘Right to Explanation’,” AI Magazine, vol. 38, no. 3, pp. 50-57, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Privacy Sandbox, Protect User Privacy Online. [Online]. Available: https://privacysandbox.google.com/
[5] European Commission, Data Protection. [Online]. Available: https://commission.europa.eu/law/law-topic/data-protection_en
[6] Klaus M. Miller, Karlo Lukic, and Bernd Skiera, “The Impact of GDPR on Digital Advertising: Evidence from a Large-Scale Field Study,” Information Systems Research, vol. 34, no. 2, pp. 567-589, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ren Pang et al., “A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models,” Proceedings of the 2020 ACM Conference on Computer and Communications Security, pp. 85-99, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jian Zhao et al., “MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning,” IEEE Transactions on Games, vol. 16, no. 3, pp. 556-565, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Praveena Nuthakki et al., “Deep Learning based Multilingual Speech Synthesis using Multi-Feature Fusion Methods,” ACM Transactions on Asian Low-Resource Language Information Processing, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] European Commission, Guidelines on Personal Data Breach Notification under Regulation 2016/679, 2018. [Online]. Available: https://ec.europa.eu/newsroom/article29/items/612052
[11] Cynthia Dwork, Differential Privacy,” Automata, Language and Programming, pp. 1-12, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Qian Xiao, Rui Chen, and Kian-Lee Tan, “Differentially Private Data Release via Structural Inference,” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 911-920, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Tian Li et al., “Federated Optimization in Heterogeneous Networks,” Proceedings of Machine Learning and Systems, vol. 2, 2020.
[Google Scholar] [Publisher Link]
[14] Peter Kairouz et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends in Machine Learning, vol. 14, no. 1-2, pp. 1-210, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jiang Bian et al., “Feynman: Federated Learning-Based Advertising for Ecosystems-Oriented Mobile Apps Recommendation,” IEEE Transactions on Service Computing, vol. 16, no. 5, pp. 3361-3372, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Meta Research, Private Computation Framework 2.0. [Online]. Available: https://research.facebook.com/publications/private computation-framework-2-0/ [17] Zili Lu et al., “Federated Learning with Non-IID Data: A Survey,” IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19188-19209, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Samira Silva, Patrizio Pelliccione, and Antonia Bertolino, “Self-Adaptive Testing in the Field,” ACM Transactions on Autonomous and Adaptive Systems, vol. 19, no. 1, pp. 1-37, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Google Ads Help, Results from Our Display Ads Experiment with the Privacy Sandbox APIs, 2024. [Online]. Available: https://support.google.com/google-ads/answer/15192137?hl=en
[20] Apple Inc., App Tracking Transparency. [Online]. Available: https://developer.apple.com/documentation/apptrackingtransparency