基于自适应差分隐私机制的工业数据安全研究与发展

曲升宇

doi:10.11959/j.issn.1000-0801.2026092

您当前的位置：

首页 >

文章列表页 >

基于自适应差分隐私机制的工业数据安全研究与发展

研究与开发 | 更新时间：2026-04-08

- 基于自适应差分隐私机制的工业数据安全研究与发展
- Research and development of industrial data security based on adaptive differential privacy mechanisms
- 电信科学 2026年42卷第3期页码：44-53
- 作者机构：
  
  中国工业互联网研究院（工业和信息化部密码应用研究中心），北京 100015
- 作者简介：
  
  [ "曲升宇（1991- ），男，中国工业互联网研究院工程师、研究员，主要研究方向为工业互联网、数字经济。" ]
- 基金信息：
  
  工业和信息化部2021年产业技术基础公共服务平台项目(2021-H026-1-1)
- DOI：10.11959/j.issn.1000-0801.2026092
  中图分类号： TP393
- 收稿：2025-10-11，
  
  修回：2025-11-03，
  
  录用：2025-11-18，
  
  纸质出版：2026-03-20
- 稿件说明：
移动端阅览
曲升宇.基于自适应差分隐私机制的工业数据安全研究与发展[J].电信科学,2026,42(03):44-53.

Qu Shengyu.Research and development of industrial data security based on adaptive differential privacy mechanisms[J].Telecommunications Science,2026,42(03):44-53.
曲升宇.基于自适应差分隐私机制的工业数据安全研究与发展[J].电信科学,2026,42(03):44-53. DOI： 10.11959/j.issn.1000-0801.2026092.

Qu Shengyu.Research and development of industrial data security based on adaptive differential privacy mechanisms[J].Telecommunications Science,2026,42(03):44-53. DOI： 10.11959/j.issn.1000-0801.2026092.

摘要

针对工业数据的高度敏感性、强关联性与实时性要求，提出自适应差分隐私（adaptive differential privacy，ADP）作为一种面向动态环境的隐私保护新范式。系统阐述ADP的理论演进与工业适配性，凝练出动态隐私预算调度、关联敏感度估计、隐私-效用均衡优化3条核心技术路径，并且结合工业控制系统、供应链协同、预测性维护三大典型场景验证其有效性。研究结果表明，ADP能够突破静态差分隐私的“效用-隐私”权衡困境，在复杂工业环境中实现隐私保护与数据价值的协同优化。未来，紧致隐私分析、高维异构数据处理、鲁棒性设计及标准化生态构建是工业数据安全的主要研究与应用方向。

Abstract

In response to the high sensitivity

strong correlation

and real‑time requirements of industrial data

an adaptive differential privacy (ADP) framework was proposed as a new paradigm for privacy protection in dynamic environments. The theoretical evolution and industrial adaptability of ADP were systematically elaborated. Three core technical pathways were distilled: dynamic privacy budget scheduling

correlation‑aware sensitivity estimation

and privacy‑utility balance optimization. The effectiveness of the approach was validated through three typical industrial scenarios: industrial control systems

supply‑chain collaboration

and predictive maintenance. The results demonstrate that ADP can overcome the “utility‑privacy” trade‑off inherent in static differential privacy

enabling synergistic optimization of privacy preservation and data value extraction in complex industrial settings. Finally

it was pointed out that compact privacy analysis

efficient processing of high‑dimensional heterogeneous data

robustness design

and the construction of a standardized ecosystem represented key future directions for research and application in industrial data security.

关键词

Keywords

references

Liu Y , Chi C , Zhang Y W , et al . Identification and resolution for industrial Internet: architecture and key technology [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 18 ): 16780 - 16794 .

卓振宇 , 张宁 , 谢小荣 , 等 . 高比例可再生能源电力系统关键技术及发展挑战 [J ] . 电力系统自动化 , 2021 , 45 ( 9 ): 171 - 191 .

Zhuo Z Y , Zhang N , Xie X R , et al . Key technologies and developing challenges of power system with high proportion of renewable energy [J ] . Automation of Electric Power Systems , 2021 , 45 ( 9 ): 171 - 191 .

Liu Y , Xie B , Han T Y , et al . Modeling identifiable data in industrial Internet [J ] . IEEE Access , 2020 , 8 : 29140 - 29148 .

Xie G X , Hou G P , Pei Q Q , et al . Lightweight privacy protection via adversarial sample [J ] . Electronics , 2024 , 13 ( 7 ): 1230 .

IBM Security . 2023 cost of a data breach report [R ] . 2023 .

熊世强 , 何道敬 , 王振东 , 等 . 联邦学习及其安全与隐私保护研究综述 [J ] . 计算机工程 , 2024 , 50 ( 5 ): 1 - 15 .

Xiong S Q , He D J , Wang Z D , et al . Review of federated learning and its security and privacy protection [J ] . Computer Engineering , 2024 , 50 ( 5 ): 1 - 15 .

Chen Z Y , Zheng H , Liu G , et al . AWDP-FL: an adaptive differential privacy federated learning framework [J ] . Electronics , 2024 , 13 ( 19 ): 3959 .

Huang H B , Yan M , Yan Q , et al . Research and implementation of a classification method of industrial big data for security management [J ] . Transactions on Emerging Telecommunications Technologies , 2024 , 35 ( 11 ): e70021 .

Soria-Comas J , Domingo-Ferrer J , Sánchez D , et al . Individual differential privacy: a utility-preserving formulation of differential privacy guarantees [J ] . IEEE Transactions on Information Forensics and Security , 2017 , 12 ( 6 ): 1418 - 1429 .

Feng X J , Zhang C P . MPLDP: multi-level personalized local differential privacy method [J ] . IEEE Access , 2024 , 12 : 99739 - 99754 .

Rajpurohit T , Haddad W M . Lyapunov and converse Lyapunov theorems for stochastic semistability [J ] . Systems & Control Letters , 2016 , 97 : 83 - 90 .

Pasa L , Navarin N , Sperduti A . Multiresolution reservoir graph neural network [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 33 ( 6 ): 2642 - 2653 .

Tang C W , Yang X K , Zhai G T . Robust noise estimation based on noise injection [J ] . Journal of Signal Processing Systems , 2014 , 74 ( 1 ): 69 - 78 .

Bai C , Li H Q . Simultaneous prediction in the generalized linear model [J ] . Open Mathematics , 2018 , 16 ( 1 ): 1037 - 1047 .

Wang Z M , Ding Y R , Jin X M , et al . Task offloading for edge computing in industrial Internet with joint data compression and security protection [J ] . The Journal of Supercomputing , 2023 , 79 ( 4 ): 4291 - 4317 .

Wang Y F , Li S B , Chen K K , et al . Privacy-preserving incentive allocation for fair and resilient data sharing in resource-constrained edge computing networks [J ] . Mathematics , 2025 , 13 ( 3 ): 422 .

Wang S N , Li C G . Distributed stochastic algorithm for global optimization in networked system [J ] . Journal of Optimization Theory and Applications , 2018 , 179 ( 3 ): 1001 - 1007 .

Amouzou G , Atchonouglo K , Holweck F . Phase sensitivity of entanglement in the Quantum Phase Estimation algorithm [J ] . Physica Scripta , 2024 , 99 ( 9 ): 095122 .

Ahmed M O , Vaswani S , Schmidt M . Combining Bayesian optimization and Lipschitz optimization [J ] . Machine Learning , 2020 , 109 ( 1 ): 79 - 102 .

Zhao D Y , Wang M , Zhao D Y , et al . Smart manufacturing promotes high-quality development of enterprises in China [J ] . Sustainability , 2024 , 16 ( 23 ): 10431 .

Eesee A K , Varga V , Eigner G , et al . Impact of work instruction difficulty on cognitive load and operational efficiency [J ] . Scientific Reports , 2025 , 15 : 11028 .

Kenett R S , Gotwalt C . The analysis of association rules: sensitivity analysis [J ] . Applied Stochastic Models in Business and Industry , 2025 , 41 ( 3 ): e70022 .

Li T Y , Wang B H , Shang F T , et al . Threat model and construction strategy on ADS-B attack data [J ] . IET Information Security , 2020 , 14 ( 5 ): 542 - 552 .

Hamdipoor V , Meskin N , Cassandras C G . Safe control synthesis using environmentally robust control barrier functions [J ] . European Journal of Control , 2023 , 74 : 100840 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

边缘算力发展态势分析

面向工业互联网的5G空口双发选收技术应用研究

面向垂直行业专网的确定性SLA指标分级研究

时间敏感网络中的冗余机制研究综述