私人措施,随机步行和合成数据.
March Boedihardjo1, Thomas Strohmer2, Roman Vershynin3
1Department of Mathematics, Michigan State University, East Lansing, USA.
概括
这项研究引入了度量隐私,这是差异隐私的强有力的概括,以创建准确的私人合成数据用于各种分析. 它克服了现有方法的局限性,增强了机器学习任务的隐私保护数据共享.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
- 机器学习 机器学习
背景情况:
- 不同隐私为数据共享提供了信息理论上的安全性.
- 现有的差异性隐私机制对特定查询和复杂的机器学习任务的实用性保证有局限性.
- 需要支持更广泛的数据分析和机器学习的隐私方法.
研究的目的:
- 为了克服当前差异性隐私技术的局限性.
- 开发一种用于生成准确的私有合成数据的方法,适用于广泛的统计分析.
- 加强机器学习任务的隐私保证,如集群和分类.
主要方法:
- 使用度量隐私,是对差异隐私的概括.
- 开发了一个多项式时间算法,从数据集中创建一个"私人测量".
- 引入了一种新的"超常规随机步行"作为构建中的关键组件.
主要成果:
- 成功地为各种统计分析工具准确地构建了私有合成数据.
- 在紧的度量空间中,证明了对私有测量和合成数据的异常清晰的最小-最大结果.
- 超规律的随机步行表现出类似于独立变量的规律性,同时从原点缓慢偏离.
结论:
- 拟议的度量隐私方法有效生成准确的私人合成数据.
- 这种方法克服了传统差异隐私的显著局限性,使得更广泛的应用.
- 这些发现推动了保护隐私的数据分析和机器学习.
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