FedDroidMeter:基于FL的Android恶意软件分类系统的隐私风险评估器
Changnan Jiang1, Chunhe Xia1,2, Zhuodong Liu1
1Key Laboratory of Beijing Network Technology, Beihang University, Beijing 100191, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
我们介绍FedDroidMeter,这是一个新的框架,用于评估联邦学习 (FL) 安卓恶意软件分类器中的隐私风险. 这个工具可以测量敏感数据泄露,提高机器学习模型中用户的隐私.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 保护隐私的技术 保护隐私的技术
背景情况:
- 传统的Android恶意软件分类器收集敏感的用户数据,造成隐私风险.
- 联合学习 (FL) 提供了一个保护隐私的替代方案,但仍然容易受到间接隐私推断的影响.
- 现有的隐私评估方法缺乏基于FL的恶意软件检测的综合方法.
研究的目的:
- 提出FedDroidMeter,一个基于FL的Android恶意软件分类器的隐私风险评估框架.
- 在这些分类器中测量与敏感信息披露相关的隐私风险.
- 为评估和比较不同FL配置中的隐私风险提供系统方法.
主要方法:
- 开发FedDroidMeter基于规范化的相互信息来量化隐私风险.
- 设计了框架,使其独立于特定的攻击模型和能力.
- 使用Androzoo数据集和基线FL分类器进行了数值评估.
主要成果:
- 在基于FL的Android恶意软件分类器中,FedDroidMeter有效地测量隐私风险.
- 该框架允许在不同模型,FL设置和隐私参数中平等比较隐私风险.
- 在这些分类器中,初步研究探索了管理隐私风险的基本规律.
结论:
- FedDroidMeter提供了一个重要的系统框架,用于评估基于FL的恶意软件分类器中的隐私风险.
- 这些发现强调了隐私风险评估在开发安全的FL系统中的重要性.
- 这项研究为开发有针对性的隐私保护方法提供了理论基础和实践经验.
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