成员推断攻击对差异私有区块坐标下降的攻击
Shazia Riaz1,2, Saqib Ali2,3, Guojun Wang3
1School of Computing, Macquarie University, Sydney, Australia.
PeerJ. Computer science
|October 23, 2023
概括
像DP-BCD这样的差异隐私 (DP) 方法在深度学习中保护敏感数据. 这项研究证实DP-BCD有效地防止了成员推断攻击,同时保持了模型的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习模型依赖于大型数据集,通常包含敏感的个人信息.
- 隐私问题源于这些数据的潜在滥用,推动了对保护隐私的深度学习的研究.
- 差异隐私 (DP) 是深度学习模型中保护数据的关键技术.
研究的目的:
- 分析评估DP-BCD的有效性,这是一个新的隐私保护方法,针对复杂的隐私攻击.
- 评估DP-BCD在黑盒和白盒设置中的实际隐私保护能力.
- 将DP-BCD的性能与最先进的DP-SGD方法进行比较.
主要方法:
- 在黑子和白子场景中实施的会员推断攻击 (MIA).
- 在基准数据集上评估DP-BCD和DP-SGD.
- 使用的性能指标包括AUC,攻击者优势,精度,回忆和F1分数.
主要成果:
- DP-BCD展示了强大的隐私保护对先进的对手.
- 与现有技术相比,该方法保持了可接受的模型实用性.
- 实验结果验证了DP-BCD能够抵抗复杂的隐私攻击的能力.
结论:
- DP-BCD有效地保护了深度学习模型的隐私,防止会员推断攻击.
- DP-BCD为DP-SGD提供了一个有前途的替代方案,隐私成本低,准确度高.
- 该研究证实了DP-BCD在保护敏感培训数据方面的实际可行性.
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