南京大学学报(自然科学版) ›› 2023, Vol. 59 ›› Issue (6): 970–980.doi: 10.13232/j.cnki.jnju.2023.06.007

• • 上一篇    下一篇

基于三支决策的新型分类匿名模型

蒋浩英1, 钱进1,2(), 王滔滔1, 洪承鑫1, 余鹰1   

  1. 1.华东交通大学软件学院，南昌，330013
    2.江苏科技大学计算机学院，镇江，212003
  • 收稿日期:2023-08-10 出版日期:2023-11-30 发布日期:2023-12-06
  • 通讯作者: 钱进 E-mail:qjqjlqyf@163.com
  • 基金资助:
    国家自然科学基金(62066014);江西省双千计划，江西省自然科学基金(20202BABL202018);江西省研究生创新专项基金(YC2022?s498)

A novel classified anonymity model based on the three⁃way decisions

Haoying Jiang1, Jin Qian1,2(), Taotao Wang1, Chengxin Hong1, Ying Yu1   

  1. 1.School of Software, East China Jiaotong University, Nanchang, 330013, China
    2.School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
  • Received:2023-08-10 Online:2023-11-30 Published:2023-12-06
  • Contact: Jin Qian E-mail:qjqjlqyf@163.com

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摘要：

数据匿名技术是目前应用最广泛的隐私保护技术，可以在保护数据私密性的同时最大限度地保证数据的可用性和计算的高效性.然而，现有的数据匿名模型采用的都是二分类的匿名模式，这种非此即彼的处理方式往往过度偏激，造成大量不必要的信息损失.针对这个问题，提出一种基于三支决策的新型分类匿名模型.首先，在k??匿名模型的基础上，提出匿名上、下限以及模糊数据的概念；其次，将三支决策的思想引入数据匿名过程，通过延迟决策的方式考虑实际决策过程中可能出现的边缘性的模糊数据，提出一种新型的三支分类匿名模型，即Uk,Lk?分类匿名模型；然后，为了验证所提模型的可用性，结合差分隐私的思想，在延迟决策中使用添加噪声的方式对模糊数据进行再处理.实验结果证明，提出的模型可以很好地提高数据可用性，在实际应用场景中的适用性更强.

Abstract:

Data anonymization technology is the most widespread data privacy protection technology as it maximizes data availability and computational efficiency while protecting data privacy. However，existing data anonymization models adopt the binary classification anonymity model，and this either?or treatment is often overly biased，resulting in massive unnecessary information loss. To address this problem，this paper combines the idea of three?way decisions and proposes a novel classification anonymity model based on three?way decisions. Firstly，we propose the concept of anonymous upper，lower bounds and fuzzy data on the basis of k?anonymity model. Secondly，the idea of three?way decisions is introduced into the data anonymization，and the marginal fuzzy data that may appear in the actual decision process is considered by delaying the decision. A novel three?way classified anonymity model，the Uk,Lk?classified anonymity model is proposed. Then，in order to verify the usability of the proposed model，the fuzzy data are reprocessed by adding noise in the delayed decision in combination with the idea of differential privacy. Finally，experimental results demonstrate that the proposed model improves the data availability well and is more applicable in practical application scenarios.

中图分类号: 

  • TP309
1 Song F G， Ma T H， Tian Y，et al. A new method of privacy protection：Random k?anonymous. IEEE Access，2019(7)：75434-75445.
2 Ali W， Nauman M， Azam N. A privacy enhancing model for Internet of Things using three?way decisions and differential privacy. Computers and Electrical Engineering，2022(100)：107894.
3 方凯，史志才，贾媛媛. 基于混合聚类的k ?匿名数据发布算法. 电子科技，2022，35(12)：78-83.
3 Fang K， Shi Z C， Jia Y Y. K?anonymity data publishing algorithm based on hybrid clustering. Electronic Science and Technology，2022，35(12)：78-83.
4 Sweeney L. K?anonymity：A model for protecting privacy. International Journal of Uncertainty，Fuzziness and Knowledge?Based Systems，2002，10(5)：557-570.
5 Machanavajjhala A， Kifer D， Gehrke J，et al. L?diversity：Privacy beyond k?anonymity. ACM Transactions on Knowledge Discovery from Data，2007，1(1)：3-es.
6 Wong R C W， Li J Y， Fu A W C，et al. ( α , k ) ? anonymity：An enhanced k?anonymity model for privacy preserving data publishing∥Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia，PA，USA：ACM，2006：754-759.
7 Li N H， Li T C， Venkatasubramanian S. t?Closeness：Privacy Beyond k?Anonymity and l?Diversity∥Proceedings of 2007 IEEE 23rd International Conference on Data Engineering. Istanbul，Turkey：IEEE，2007：106-115.
8 Liang Y T， Samavi R. Optimization?based k?anonymity algorithms. Computers & Security，2020(93)：101753.
9 张强，叶阿勇，叶帼华，等. 最优聚类的k ?匿名数据隐私保护机制. 计算机研究与发展，2022，59(7)：1625-1635.
9 Zhang Q， Ye A Y， Ye G H，et al. k?anonymous data privacy protection mechanism based on optimal clustering. Journal of Computer Research and Development，2022，59(7)：1625-1635.
10 翟冉，陈学斌，张国鹏，等. 基于不同敏感度的改进k ?匿名隐私保护算法. 计算机应用，2023，43(5)：1497-1503.
10 Zhai R， Chen X B， Zhang G P，et al. Improved k?anonymity privacy protection algorithm based on different sensitivity. Journal of Computer Applications，20223，43(5)：1497-1503.
11 Kacha L， Zitouni A， Djoudi M. KAB：A new k?anonymity approach based on black hole algorithm. Journal of King Saud University：Computer and Information Sciences，2022，34(7)：4075-4088.
12 Mehta B B， Rao U P. Improved l?diversity：Scalable anonymization approach for privacy preserving big data publishing. Journal of King Saud University：Computer and Information Sciences，2022，34(4)：1423-1430.
13 Yao Y Y. Three?way decisions with probabilistic rough sets. Information Sciences，2010，180(3)：341-353.
14 Liang D C， Pedrycz W， Liu D，et al. Three?way decisions based on decision?theoretic rough sets under linguistic assessment with the aid of group decision making. Applied Soft Computing，2015(29)：256-269.
15 Yao Y Y. Three?way decision and granular computing. International Journal of Approximate Reasoning，2018(103)：107-123.
16 Yao Y Y. Three?way granular computing，rough sets，and formal concept analysis. International Journal of Approximate Reasoning，2020(116)：106-125.
17 Li H X， Zhang L B， Huang B，et al. Sequential three?way decision and granulation for cost?sensitive face recognition. Knowledge?Based Systems，2016(91)：241-251.
18 Luo J F， Hu M J， Qin K Y. Three?way decision with incomplete information based on similarity and satisfiability. International Journal of Approximate Reasoning，2020(120)：151-183.
19 Yu H， Chen Y， Lingras P，et al. A three?way cluster ensemble approach for large?scale data. International Journal of Approximate Reasoning，2019(115)：32-49.
20 Ye X Q， Liu D. An interpretable sequential three?way recommendation based on collaborative topic regression. Expert Systems with Applications，2021(168)：114454.
21 Yao Y Y. The superiority of three?way decisions in probabilistic rough set models. Information Sciences，2011，181(6)：1080-1096.
22 Liu D. The effectiveness of three?way classification with interpretable perspective. Information Sciences，2021(567)：237-255.
23 Yue X D， Chen Y F， Yuan B，et al. Three?way image classification with evidential deep convolutional neural networks. Cognitive Computation，2022，14(6)：2074-2086.
24 Shah A， Azam N， Ali B，et al. A three?way clustering approach for novelty detection. Information Sciences，2021(569)：650-668.
25 Yuan K H， Xu W H， Li W T，et al. An incremental learning mechanism for object classification based on progressive fuzzy three?way concept. Information Sciences，2022，584：127-147.
26 Sweeney L. Achieving k?anonymity privacy protection using generalization and suppression. International Journal of Uncertainty，Fuzziness and Knowledge?Based Systems，2002，10(5)：571-588.
27 Dem?ar J. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research，2006，7：1-30.
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