Trustworthy Cross-Platform Conversion Attribution via Federated Causal Inference and Zero-Knowledge Auditing Mechanisms
Keywords:
conversion attribution, federated learning, causal inference, zero-knowledge proofs, privacy preservation, auditing, cross-platform advertisingAbstract
The increasing fragmentation of digital advertising across platforms, devices, and channels has created a pressing need for attribution systems that can track conversions reliably without violating user privacy or exposing proprietary business data. Traditional conversion attribution models rely on centralized data aggregation, which introduces significant risks of privacy breaches, data leakage, and adversarial manipulation. This paper proposes a new architecture for trustworthy cross-platform conversion attribution that combines federated causal inference with zero-knowledge auditing mechanisms. The approach enables participating platforms to collaboratively estimate attribution effects without sharing raw user-level data, while simultaneously allowing independent auditors to verify the correctness of the attribution computations without learning anything about the underlying data. We examine the structural trade-offs between statistical accuracy, computational overhead, privacy guarantees, and auditability, and discuss how such a system can be governed through regulatory frameworks and incentive-compatible protocols. The paper also addresses deployment challenges including heterogeneous data distributions, cross-device identity resolution, temporal dynamics, and adversarial robustness. By integrating causal reasoning with cryptographic verification, the proposed framework moves toward a sustainable and equitable attribution ecosystem that respects both user privacy and business confidentiality. We conclude with policy implications and future research directions for large-scale socio-technical attribution infrastructures.
References
1. Li, S., Liu, Y., Hu, Y., & Sundararajan, M. (2021). Efficient attribution of contributions to conversions. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3037–3045.
2. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
3. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
4. Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941–1973.
5. Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. Proceedings of the 29th IEEE Symposium on Security and Privacy, 111–125.
6. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
7. Vo, T. V., Lee, J., & van der Laan, M. J. (2020). Federated causal inference in high-dimensional settings. Journal of the American Statistical Association, 115(529), 415–428.
8. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Proceedings of the 3rd Conference on Theory of Cryptography, 265–284.
9. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318.
10. Goldwasser, S., Micali, S., & Rackoff, C. (1989). The knowledge complexity of interactive proof systems. SIAM Journal on Computing, 18(1), 186–208.
11. Ben-Sasson, E., Chiesa, A., Tromer, E., & Virza, M. (2014). Succinct non-interactive zero knowledge for a von Neumann architecture. Proceedings of the 23rd USENIX Security Symposium, 781–796.
12. Xu, L., Chen, H., & Shi, E. (2018). V-MAC: Verifiable and private advertising measurement. Proceedings of the 25th ACM Conference on Computer and Communications Security, 1324–1338.
13. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. Proceedings of the 37th International Conference on Machine Learning, 1597–1607.
14. Bowe, S., Chiesa, A., Green, M., Mitrus, I., & Tziallas, S. (2020). Recursive proof composition for any scheme. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 269–284.
15. Kusner, M. J., Loftus, J. R., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30, 4066–4076.
16. Shi, C., Li, S., Guo, S., Xie, S., Wu, W., Dou, J., ... & Chua, T. S. (2025). Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation. arXiv preprint arXiv:2511.17282.
17. Dwork, C., Roth, A., & et al. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.
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