对于信号估计和学习中的应用来说,近似函数与近似隐私.
Naima Tasnim1, Jafar Mohammadi2, Anand D Sarwate3
1Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka P.O. Box 1205, Bangladesh.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
我们介绍了Gaussian FM,一个新的算法,改进了功能机制,以便在数据分析中更好地进行隐私-实用性权衡. 这种方法可以显著降低噪音,提高实用性,同时保持近似差异隐私 (DP).
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 算法设计 算法设计
背景情况:
- 组织收集敏感的个人数据,需要保护隐私的算法.
- 不同隐私 (DP) 提供严格的隐私,但往往会损害数据的实用性.
- 现有的功能机制 (FM) 在DP下面面临着公用事业成本的权衡.
研究的目的:
- 开发一个改进的功能机制 (FM),提供增强的实用与差异隐私 (DP).
- 为了减少DP算法中的噪音,从而改善隐私与实用性的平衡.
- 将拟议的机制扩展到分散的数据设置.
主要方法:
- 建议高斯FM,使用高斯噪声对功能机制 (FM) 的增强.
- 通过将Gaussian FM与CAPE协议集成用于分散数据,开发了capeFM.
- 在分析和经验上分析了降噪和效用改进.
主要成果:
- 与现有的FM算法相比,Gaussian FM提供了数量级更小的噪音.
- capeFM在分散的环境中实现了与集中式方法相美的效用.
- 经验结果表明,在各种数据集上,与最先进的方法相比,其性能优越.
结论:
- 高斯 FM为数据分析提供了卓越的隐私-实用性权衡.
- 提出的方法提高了数据的实用性,同时保持了个人的隐私.
- 这些算法代表了隐私保护数据分析技术的重大进步.
相关概念视频
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