基于自动化特征组合的隐私保护风险识别机制

蔡民超; 姚宏伟; 王旸; 秦湛; 陈少梦; 任奎

doi:10.11959/j.issn.1000-436x.2024194

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基于自动化特征组合的隐私保护风险识别机制

学术论文 | 更新时间：2024-12-24

- 基于自动化特征组合的隐私保护风险识别机制
- Privacy protection risk identification mechanism based on automated feature combination
- 通信学报 2024年45卷第11期页码：1-14
- 作者机构：
  
  1.浙江大学网络空间安全学院，浙江杭州 310007
  2.杭州快迪科技有限公司，浙江杭州 310000
- 作者简介：
  
  [ "蔡民超（1991- ），男，山东烟台人，浙江大学博士生，主要研究方向为数据安全、隐私保护、反欺诈等。" ]
  [ "姚宏伟（1993- ），男，福建泉州人，浙江大学博士生，主要研究方向为可信人工智能、大模型安全等。" ]
  [ "王旸（1988- ），男，浙江嘉兴人，浙江大学博士生，主要研究方向为网络安全、数据安全等。" ]
  [ "秦湛（1988- ），男，北京人，博士，浙江大学研究员、博士生导师，主要研究方向为数据安全、隐私保护、AI安全等。" ]
  [ "陈少梦（1989- ），男，浙江宁波人，杭州快迪科技有限公司工程师，主要研究方向为数据安全、反作弊、反欺诈等。" ]
  [ "任奎（1978- ），男，安徽芜湖人，博士，浙江大学教授、博士生导师，主要研究方向为数据安全、人工智能安全等。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2021YFB3100300);国家自然科学基金资助项目(U20A20178;62072395;62206207)
- DOI：10.11959/j.issn.1000-436x.2024194
  中图分类号： TP181
- 收稿：2024-05-31，
  
  修回：2024-09-30，
  
  纸质出版：2024-11-25
- 稿件说明：
移动端阅览
蔡民超,姚宏伟,王旸等.基于自动化特征组合的隐私保护风险识别机制[J].通信学报,2024,45(11):1-14.

CAI Minchao,YAO Hongwei,WANG Yang,et al.Privacy protection risk identification mechanism based on automated feature combination[J].Journal on Communications,2024,45(11):1-14.
蔡民超,姚宏伟,王旸等.基于自动化特征组合的隐私保护风险识别机制[J].通信学报,2024,45(11):1-14. DOI： 10.11959/j.issn.1000-436x.2024194.

CAI Minchao,YAO Hongwei,WANG Yang,et al.Privacy protection risk identification mechanism based on automated feature combination[J].Journal on Communications,2024,45(11):1-14. DOI： 10.11959/j.issn.1000-436x.2024194.

摘要

异常行为识别（AD）算法在实际应用中，通常会面临特征组合优化困难、分类器准确率难提高、模型应用效率低等技术挑战。用户所产生的多维数据具有丰富的空间结构信息，围绕这些多维数据的特点，在通过同态加密的隐私保护方式进行数据脱敏的基础上，针对特征组合优化困难的技术挑战，提出并实现了首个基于特征分箱的自动化特征组合优化模型算法，该算法在特征组合优化方面提升了99.93%的计算效率。基于自动化特征组合优化模型筛选出的重要特征所组合的规则仍存在分类器准确率难提高的技术挑战，故将自动化筛选出的重要特征融入识别模型中，设计并实现了首个规则和算法的交叉应用模型，并将该方式应用到基于用户多维信息的异常行为识别中，在识别先享不付类异常用户的具体场景中实现资金挽损效率提升27.78%。

Abstract

In practice

the anomaly detection (AD) algorithm usually faced technical challenges such as difficulty in optimizing feature combinations

difficulty in improving classifier accuracy

and low model application efficiency. The multidimensional data generated by users was with rich spatial structure information

revolved around the characteristics of the multidimensional data. Building upon the privacy protection method using homomorphic encryption

the technical challenge of optimizing feature combinations was addressed. The first automated feature combination optimization model algorithm based on feature binning was proposed and implemented. This algorithm enhanced computational efficiency in feature combination optimization by 99.93%. The rules combined by the important features selected by the automatic feature combination optimization model still faced the technical challenge of difficulty in improving the classifier accuracy. Therefore

the important features selected automatically were integrated into the recognition model

the first cross-application model of rules and algorithms was designed and implemented. This approach was applied to anomaly detection based on multi-dimensional user data

resulting in a 27.78% increase in funds saved in the specific scenario of identifying abnormal users who enjoy first but do not pay.

关键词

Keywords

references

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