Personal data privacy protection method based onvertical partitioning

RUAN Huafeng; LI Rui; LUO Kailun

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

您当前的位置：

首页 >

文章列表页 >

Personal data privacy protection method based onvertical partitioning

Papers | 更新时间：2024-12-20

- Personal data privacy protection method based onvertical partitioning
- Chinese Journal of Network and Information Security Vol. 10, Issue 5, Pages: 175-187(2024)
- 作者机构：
  
  东莞理工学院，广东东莞 523808
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Project of China(2021YFB3101303);The National Natural Science Foundation of China(61972089;62206055)
- DOI：10.11959/j.issn.2096-109x.2024076
  CLC： TP393
- Received：29 January 2024，
  
  Revised：2024-05-15，
  
  Published：25 October 2024
- 稿件说明：
移动端阅览
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

DU M X , WANG Q , HE M Q , et al . Privacy-preserving indexing and query processing for secure dynamic cloud storage [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 9 ): 2320 - 2332 .

SINGH P , PANDEY A K . A review on cloud data security challenges and existing countermeasures in cloud computing [J ] . International Journal of Data Informatics and Intelligent Computing , 2022 , 1 ( 2 ): 23 - 33 .

DHINAKARAN D , JOE PRATHAP P M . Preserving data confidentiality in association rule mining using data share allocator algorithm [J ] . Intelligent Automation & Soft Computing , 2022 , 33 ( 3 ): 1877 - 1892 .

MEHROTRA S , SHARMA S , ULLMAN J , et al . Partitioned data security on outsourced sensitive and non-sensitive data [C ] // Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2019 : 650 - 661 .

ORIOL F , RIBES-GONZÁLEZ J , RICCI S . Privacy-preserving data splitting: a combinatorial approach [J ] . Designs, Codes and Cryptography , 2021 , 89 ( 7 ): 1735 - 1756 .

BADRAN S , ARMAN N , FARAJALLAH M . An efficient approach for secure data outsourcing using hybrid data partitioning [C ] // Proceedings of the 2021 International Conference on Information Technology (ICIT) . Piscataway : IEEE Press , 2021 : 418 - 423 .

SOHAL M , BHARANY S , SHARMA S , et al . A hybrid multi-cloud framework using the IBBE key management system for securing data storage [J ] . Sustainability , 2022 , 14 ( 20 ): 13561 .

CIRIANI V , DE CAPITANI DI VIMERCATI S , FORESTI S , et al . Combining fragmentation and encryption to protect privacy in data storage [J ] . ACM Transactions on Information and System Security , 2010 , 13 ( 3 ): 1 - 33 .

SWEENEY L . K-anonymity: a model for protecting privacy [J ] . International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 2002 , 10 ( 5 ): 557 - 570 .

GUO W X , ZHEN H L , LI X J , et al . Machine learning methods in solving the Boolean satisfiability problem [J ] . Machine Intelligence Research , 2023 , 20 ( 5 ): 640 - 655 .

MEHROTRA S , OKTAY K Y , SHARMA S . Exploiting data sensitivity on partitioned data [M ] // Lecture Notes in Computer Science . Cham : Springer International Publishing , 2018 : 274 - 299 .

AGGARWAL G , BAWA M , GANESAN P , et al . Two can keep a secret: a distributed architecture for secure database services [J ] . 2nd Biennial Conference on Innovative Data Systems Research , CIDR 2005 , 2005 : 186 - 199 .

YANG J J , LI J Q , NIU Y . A hybrid solution for privacy preserving medical data sharing in the cloud environment [J ] . Future Generation Computer Systems , 2015 , 43 : 74 - 86 .

GANAPATHY V , THOMAS D , FEDER T , et al . Distributing data for secure database services [C ] // Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society . New York : ACM , 2011 : 1 – 10 .

CIRIANI V , DE CAPITANI DI VIMERCATI S , FORESTI S , et al . Selective data outsourcing for enforcing privacy [J ] . Journal of Computer Security , 2011 , 19 ( 3 ): 531 - 566 .

SHARAD M , SHARMA S , ULLMAN J , et al . Partitioned data security on outsourced sensitive and non-sensitive data [C ] // Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2019 : 650 - 661 .

SUDHA R , POOJA G , REVATHY V , et al . Advances in parallel computing [M ] . Amsterdam : IOS Press , 2021 : 854 - 858 .

DENIS N , CHAFFARDON P , CONAN D , et al . Privacy-preserving content-based publish/subscribe with encrypted matching and data splitting [C ] // Proceedings of the 17th International Joint Conference on e-Business and Telecommunications . SCITEPRESS - Science and Technology Publications , 2020 : 405 – 414 .

ATTASENA V , DARMONT J , HARBI N . Secret sharing for cloud data security: a survey [J ] . The VLDB Journal , 2017 , 26 ( 5 ): 657 - 681 .

ASUNCION A , NEWMAN D . UCI machine learning repository [R ] . 2007 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Anomaly detection in DNS: an empirical study

Research progress on detection of software vulnerabilities based on code similarity

Bidirectional privacy authorization for large models based on private set intersection

method for machine learning backdoor attacks based on defense method based on diffusion models

Research on searchable encryption scheme based on enhanced PIR

Related Author

HOU Ronghao

HE Ming

KONG Kaichuan

CHEN Yong

ZUO Peng

LI Qinghao

LIU Chunling

TANG Yonghe

Related Institution

China Internet Network Information Center

School of Cyber Science and Engineering, Jinan University

Information Engineering University

Key Laboratory of Counter-Terrorism Command Information Engineering of the Ministry of Education, Engineering University of PAP

College of Cryptography Engineering, Engineering University of PAP

⁰