A Review of the Principles and Typical Applications of Differential Privacy Technology

Ketong Liu

doi:10.47297/taposatWSP2633-456920.20250609

PDF
导出
分享
收藏
专辑

A Review of the Principles and Typical Applications of Differential Privacy Technology

6卷第9期

浏览量: 0 Downloads: 4

A Review of the Principles and Typical Applications of Differential Privacy Technology
A Review of the Principles and Typical Applications of Differential Privacy Technology
Reading College, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
2025年6卷第9期页码：97-101
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456920.20250609
Accepted：

Scan QR Code

Ketong Liu. (2025). A Review of the Principles and Typical Applications of Differential Privacy Technology. 科技理论与实践（英文版）, 6(9), 97-101.

Liu Ketong.A Review of the Principles and Typical Applications of Differential Privacy Technology[J].Theory and Practice of Science and Technology,2025,06(09):97-101.
Ketong Liu. (2025). A Review of the Principles and Typical Applications of Differential Privacy Technology. 科技理论与实践（英文版）, 6(9), 97-101. DOI： 10.47297/taposatWSP2633-456920.20250609.

Liu Ketong.A Review of the Principles and Typical Applications of Differential Privacy Technology[J].Theory and Practice of Science and Technology,2025,06(09):97-101. DOI： 10.47297/taposatWSP2633-456920.20250609.

摘要

Abstract

With the vigorous development of big data and artificial intelligence technologies

data sharing and analysis have become increasingly important in various fields. However

the accompanying risk of privacy leakage has emerged as an urgent issue to be addressed

[

]

. As a privacy protection framework with a rigorous mathematical foundation

differential privacy provides an effective solution for balancing data utilization and privacy security

[

]

. This paper systematically examines the core technical principles of differential privacy

including its mathematical definition

noise mechanisms

and extended models

and conducts a comparative analysis of its differences from traditional privacy protection models. Meanwhile

combined with typical application cases in fields such as healthcare and industry

it explores technical selection strategies in different scenarios. Finally

it looks forward to the future research directions of differential privacy in addressing challenges such as unstructured data protection

long - time - series data privacy

and cross - technology integration.

关键词

Keywords

references

Li, F. H., Li, H., Jia, Y., et al. Research Scope and Development Trend of Privacy Computing [J]. Journal on Communications, 2016, 37(4): 1–11. DOI:10.11959/j.issn.1000-436x.2016078.

Dwork, C. (2006). Differential Privacy [C]. In ICALP 2006 (pp. 1–12). Springer.

Dwork, C., & Roth, A. (2013). The Algorithmic Foundations of Differential Privacy [J]. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.

Jia, J. Y., Tan, C., Liu, Z. W., et al. Privacy Perspective and Privacy Goals of Differential Privacy [J]. Chinese Journal of Network and Information Security, 2023, 9(5): 82–91.

Li, S. H., Dai, M. C., & Cai, W. Z. Research on Data Desensitization Technology Based on Differential Privacy [J]. Network Security and Data Governance, 2025, 44(2): 39–43.

Zhao, Y. Q., & Yang, M. Research Progress of Differential Privacy [J]. Computer Science, 2023, 50(4): 265–276.

Sun, Y. F., Zhang, R., Tao, Y., et al. A Survey of Local Differential Privacy [J]. Frontiers of Data and Computing, 2023, 5(5): 74–97.

Zhu, T., Li, G., Zhou, W., & Yu, P. S. (2017). Differentially Private Data Publishing and Analysis: A Survey [J]. IEEE Transactions on Knowledge and Data Engineering, 29(6), 1619–1638.

Li, X. G., Li, H., Li, F. H., et al. A Survey of Differential Privacy [J]. Chinese Journal of Information Security, 2018, 3(5): 92–104.

Deng, H., Song, F. Y., Fu, L., et al. Survey on Data Security and Privacy Protection in Cloud Computing Environment [J]. Journal of Hunan University (Natural Sciences), 2022, 49(4): 1–10.

Dwork, C., Pitassi, T., Naor, M., & Rothblum, G. N. (2010). Differential Privacy under Continual Observation [C]. In STOC 2010 (pp. 715–724). ACM.

Yang, Y. L., Zhou, Y. J., & Ning, H. Research on Image Data Mining Method Supporting Differential Privacy [J]. Journal of Data Acquisition and Processing, 2021, 36(1): 85–94.

McSherry, F., & Mironov, I. (2009). Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders [C]. In KDD 2009 (pp. 627–636). ACM.

Beijing Institute of Technology. Key Information Protection Method for Blockchain - Enabled Supply Chain System Based on Differential Privacy: CN202011395328.X [P]. 2021 - 03 - 16.

Hu, H. Research on User Electricity Data Clustering Algorithm under Differential Privacy Protection [D]. North China Electric Power University, North China Electric Power University (Baoding), 2018.

Zhu, T., et al. (2017). Privacy - Preserving Image Data Publishing with Differential Privacy [J]. IEEE Transactions on Image Processing, 26(2), 876–887.

Dwork, C. (2012). Differential Privacy and the Power of (Formalizing) Negative Thinking [C]. In POST 2012 (pp. 1–2). Springer.

Liu, Y. Research on Key Technologies of Data Sharing Security Based on Blockchain and Differential Privacy [D]. Henan: Zhengzhou University, 2022.

Xing, C. B. Research on Healthcare Privacy Data Sharing Scheme Based on Blockchain and Federated Learning [D]. Hebei: Hebei University, 2024.

文章被引用时，请邮件提醒。

Submit

Privacy Protection of Clustering High-Dimensional Data Based on African Vultures

Related Author

Chenglin Zhang School of Information & Electronics Technology, Jiamusi University

Lili He School of Information & Electronics Technology, Jiamusi University

Related Institution

School of Information & Electronics Technology, Jiamusi University

⁰