协差信息的可访问性在联合学习框架中造成了脆弱性
Manuel Huth1,2, Jonas Arruda2, Roy Gusinow1,2
1Institute of Computational Biology, Helmholtz Munich, Neuherberg 85764, Germany.
Bioinformatics (Oxford, England)
|August 30, 2023
概括
一个新的攻击可以重建联邦学习 (FL) 系统中的私人数据,即使有噪音防御. 这种方法利用了基本的数据功能,突出了当前FL框架中的漏洞.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 能够在没有集中敏感信息的情况下进行协作数据分析,特别是在医疗保健领域.
- 尽管有隐私方面的好处,但FL系统很容易受到数据泄露攻击.
- 现有的FL框架可能无法充分保护人们免受复杂的隐私侵犯.
研究的目的:
- 介绍一个新的攻击算法用于数据重建在联合学习.
- 评估这种攻击对共同防御策略的有效性.
- 确定当前FL框架中的局限性,并提出改进建议.
主要方法:
- 开发了一个新的攻击算法,利用样本平均值,协差值和线性独立向量.
- 对基于随机噪音的防御机制评估了攻击的强度.
- 分析了差异性隐私作为潜在的防御策略的影响.
主要成果:
- 证明 FL 中的基本数据功能足以进行数据重建.
- 拟议的攻击能够抵御通常用于隐私保护的随机噪音.
- 确定了已建立的FL框架的隐私保证的重大限制.
结论:
- 目前的联合学习框架容易通过基于共变量的攻击泄露数据.
- 现有的防御机制,包括随机噪声,提供了不足的保护.
- 进一步研究强大的防御策略,可能涉及差异性隐私,对于安全的FL实施至关重要.
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