基于垂直分割的个人数据隐私保护方法

阮华锋; 李睿; 罗凯伦

doi:10.11959/j.issn.2096-109x.2024076

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基于垂直分割的个人数据隐私保护方法

学术论文 | 更新时间：2024-12-20

- 基于垂直分割的个人数据隐私保护方法
- Personal data privacy protection method based onvertical partitioning
- 网络与信息安全学报 2024年10卷第5期页码：175-187
- 作者机构：
  
  东莞理工学院，广东东莞 523808
- 作者简介：
  
  [ "阮华锋（1999- ），男，广东化州人，东莞理工学院硕士生，主要研究方向为数据存储隐私保护。" ]
  [ "李睿（1975- ），男，湖南汨罗人，东莞理工学院教授，主要研究方向为数据隐私保护。" ]
  [ "罗凯伦（1989- ），男，广东清远人，东莞理工学院讲师，主要研究方向为人工智能安全。" ]
- 基金信息：
  
  国家重点研发计划(2021YFB3101303);国家自然科学基金(61972089;62206055)
- DOI：10.11959/j.issn.2096-109x.2024076
  中图分类号： TP393
- 收稿：2024-01-29，
  
  修回：2024-05-15，
  
  纸质出版：2024-10-25
- 稿件说明：
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阮华锋, 李睿, 罗凯伦. 基于垂直分割的个人数据隐私保护方法[J]. 网络与信息安全学报, 2024, 10(5): 175-187.

Format: RUAN H F, LI R, LUO K L. Personal data privacy protection method based on vertical partitioning[J]. Chinese Journal of Network and Information Security, 2024, 10(5): 175-187.
阮华锋, 李睿, 罗凯伦. 基于垂直分割的个人数据隐私保护方法[J]. 网络与信息安全学报, 2024, 10(5): 175-187. DOI： 10.11959/j.issn.2096-109x.2024076.

Format: RUAN H F, LI R, LUO K L. Personal data privacy protection method based on vertical partitioning[J]. Chinese Journal of Network and Information Security, 2024, 10(5): 175-187. DOI： 10.11959/j.issn.2096-109x.2024076.

摘要

在分布式环境中，垂直分割是一种保护用户隐私的有效方法。然而，当前的垂直分割策略假设参与数据存储的各个云服务提供商（cloud service provider，CSP）之间不存在共谋。针对实际场景中CSP之间可能存在的共谋问题，探讨了如何在这种情况下保护用户数据隐私。假设有

个CSP参与数据存储，其中最多

个CSP可能会共谋，给出了垂直分割的(

)-安全定义，并提出了MLVP（machine learning vertical partitioning）方案。该方案利用机器学习算法分析属性之间的关联性，对得到的所有关联性进行优化，并将计算垂直分割方法问题转化成可满足性问题，再利用可满足性问题求解器得到分割方法。此外，对MLVP方案的安全性进行理论分析，并在真实数据集上进行实验，比较不同机器学习算法和隐私保护强度对分割效果和性能的影响；与两个不考虑CSP存在共谋的垂直分割方案（Oriol方案和Ciriani方案）在计算速度和查询速度上进行了比较。实验结果表明：在计算速度上，因为要保证CSP共谋时的安全性，MLVP方案略慢，在查询速度上，MLVP方案相较Oriol方案和Ciriani方案分别提升了32.6%和8.8%。

Abstract

In distributed environments

vertical partitioning had been an effective method to protect user privacy. However

current vertical partitioning strategies assumed that there was no collusion among the CSPs (Cloud Service Providers) involved in data storage. This study explored how to protect user data privacy when collusion might exist between CSP. Assuming

CSP participated in data storage

with no more than

of these potentially colluding

this paper defined a (

)-security for vertical partitioning and introduced an automated computation scheme for vertical partitioning based on machine learning—the MLVP scheme. This MLVP scheme utilized machine learning algorithms to analyze the correlation between attributes

optimized all correlations

and transformed the vertical partitioning problem into a satisfiability problem

which was then solved using a satisfiability solver. Moreover

the security of the MLVP scheme was theoretically analyzed. To validate the effectiveness of the MLVP scheme

experiments were conducted on real datasets to compare the impact of different machine learning algorithms and levels of privacy protection on the effectiveness and performance of the vertical partitioning. The experiments also c

ompared the MLVP scheme with two other schemes that did not consider collusion among CSP

Oriol’s and Ciriani’s schemes

in terms of computation and query speeds. The results showed that the MLVP scheme was slightly slower in computation speed to ensure security against partial CSP collusion. However

it improved the query speed by 32.6% and 8.8% compared to the aforementioned schemes

respectively.

关键词

Keywords

references

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