A Review of Differential Privacy Applications in Enterprise Data Analytics

Xiaoxiao Zhang

doi:10.47297/wspciWSP2516-252743.20261004

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A Review of Differential Privacy Applications in Enterprise Data Analytics

Volume 10, Issue 4

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A Review of Differential Privacy Applications in Enterprise Data Analytics
A Review of Differential Privacy Applications in Enterprise Data Analytics
Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010000, China
Vol. 10, Issue 4, Pages: 219-224(2026)
Published：30 August 2026
DOI：10.47297/wspciWSP2516-252743.20261004
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Zhang Xiaoxiao.A Review of Differential Privacy Applications in Enterprise Data Analytics[J].Creativity and Innovation,2026,10(04):219-224.
DOI：

Zhang Xiaoxiao.A Review of Differential Privacy Applications in Enterprise Data Analytics[J].Creativity and Innovation,2026,10(04):219-224. DOI： 10.47297/wspciWSP2516-252743.20261004.

摘要

Abstract

Against the dual backdrop of data capitalization and increasingly stringent privacy regulations

enterprise data analytics faces a fundamental tension between "data usability" and "privacy compliance." Differential privacy

as the "gold standard" of privacy protection

offers a theoretical solution to this dilemma through its rigorous mathematical framework. However

the journey from academia to industry for differential privacy is far from complete. This paper focuses on enterprise data analytics scenarios and systematically examines the key transition of differential privacy from theory to practice. The study identifies three major gaps in current enterprise applications of differential privacy: a lack of practical guidance for privacy budget configuration

a misalignment where SQL analytics takes priority over machine learning yet has immature toolchains

and a disconnect between organizational processes and privacy engineering capabilities. This paper further analyzes the applicability of advanced directions such as adaptive differential privacy and differentially private synthetic data in enterprise contexts

and proposes the core proposition of "privacy as engineering." This paper aims to provide privacy computing researchers

data governance practitioners

and enterprise decision-makers with an integrated view that combines theoretical depth and practical insights

advancing differential privacy from "provable" to "usable."

关键词

Keywords

references

Sweeney, L. Simple Demographics Often Identify People Uniquely. Health (San Francisco), 2000, 671: 1-34.

Munilla Garrido, G., Liu, X., Matthes, F., & Song, D. Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry. arXiv preprint, arXiv: 2211.03898 , 2023.

Khavkin, M., et al. Differential Privacy Configurations in the Real World: A Comparative Analysis. IEEE Transactions on Knowledge and Data Engineering, 2025 (accepted).

Ratliff, Z., Berrios, N., & Mickens, J. Timing Attacks on Differential Privacy are Practical. Proceedings of CCS, 2025.

de Medeiros, M., et al. Verified Foundations for Differential Privacy. Proceedings of ACM PLDI, 2025.

Karmitsa, N., Airola, A., Pahikkala, T., & Pitkämäki, T. A Comprehensive Guide to Differential Privacy: From Theory to User Expectations. arXiv preprint, arXiv: 2509.03294 , 2026.

Zhan, P., Tang, P., Li, Y., Wei, P., & Guo, S. Poisoning Attacks to Local Differential Privacy for Ranking Estimation. Proceedings of CCS, 2025.

Li, X., Li, N., Wang, B., & Sun, W. Mitigating Data Poisoning Attacks to Local Differential Privacy. Proceedings of CCS, 2025.

Zhao Zirun. Research on Adaptive Data Protection Methods Based on Differential Privacy. Doctoral Dissertation, Beijing University of Posts and Telecommunications, 2025.

Yang, et al. Tabular Data Synthesis with Differential Privacy: A Survey. arXiv preprint, arXiv: 2411.03351 , 2024.

Bhusal, B., Chadha, R., Sistla, A. P., & Viswanathan, M. Approximate Algorithms for Verifying Differential Privacy with Gaussian Distributions. Proceedings of CCS, 202