可调的隐私风险评估生成对抗网络的生成对抗网络的风险评估
Bayrem Kaabachi1, Farah Briki1, Bogdan Kulynych1
1Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland.
Studies in health technology and informatics
|August 23, 2024
概括
生成对抗网络 (GAN) 可以泄露私人培训数据. 本研究引入了GAN的新隐私风险评估技术,改进了现有的会员推断攻击.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 生成对抗网络 (GAN) 擅长创建现实的合成数据.
- 然而,GAN及其输出可以无意中暴露其培训数据集中的敏感信息.
- 在部署GAN之前,评估隐私风险至关重要.
研究的目的:
- 开发一种新的,实用的技术来评估GAN中的隐私风险.
- 克服当前会员推断攻击 (MIA) 的局限性,例如强有力的假设和高的计算成本.
- 为GANs提供更全面的隐私风险评估.
主要方法:
- 在标准GAN架构中利用区分器输出.
- 模拟会员推断攻击 (MIA) 以估计隐私泄露.
- 在放射学和眼科中评估合成图像生成技术.
主要成果:
- 拟议的技术提供了对隐私威胁的更全面的了解.
- 它使得最糟糕的隐私风险估计成为可能.
- 与现有方法相比,在识别隐私攻击方面达到更高的精度.
结论:
- 这种新技术有效地评估了GAN中的隐私风险.
- 它为当前的MIA方法提供了更实用,更精确的替代方案.
- 增强合成数据生成在敏感领域的安全应用.
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