MSG:通过恶意稀疏指导从削减的神经网络窃取数据
Jing Shang1, Jian Wang1, Kailun Wang1
1Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China; School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
概括
这项研究表明常见的修剪方法削弱了数据提取攻击. 一种新的攻击,恶意稀疏指导 (MSG),通过指导参数选择,保护敏感信息,嵌入数据并抵抗修剪.
科学领域:
- 机器学习安全性
- 数据隐私
- 反对机器学习
背景情况:
- 机器学习云平台引发了隐私问题.
- 相关值编码攻击 (CVEA) 将训练数据嵌入到模型参数中.
- 模型修剪技术可以降低此类攻击的有效性.
研究的目的:
- 证明模型修剪对CVEA的影响.
- 提出一种新的数据编码攻击.
- 为了提高数据提取攻击的隐形性.
主要方法:
- 根据CVEA对常用模型修剪技术的实证评估.
- 开发恶意稀疏性指导 (MSG) 用于数据嵌入.
- 整合知识转移以保持模型在修剪后的准确性.
主要成果:
- 模型修剪显著降低了CVEA的有效性.
- 它们成功地嵌入了数据,并抵御了削减.
- 由于知识转移,MSG编码的模型在修剪后保持高精度.
结论:
- 提供强大的数据提取攻击方法,
- 知识转移增强了数据提取的隐蔽性.
- 这项研究突出了安全模型部署的新漏洞.
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