强大的本地差异性隐私机制
Milan Lopuhaä-Zwakenberg1, Jasper Goseling1
1Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
我们引入了强大的本地差异隐私 (RLDP) 来保护敏感数据. 我们的框架确保了对未知数据分布的隐私,提高了实用性,并减轻了估计错误的风险.
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 信息安全 信息安全
背景情况:
- 释放敏感数据需要强大的隐私机制.
- 标准差异隐私可以导致由于最坏情况下的假设而导致效用损失.
- 隐私泄露可能来自估计和真实数据分布之间的差异.
研究的目的:
- 引入一个强大的局部差异隐私 (RLDP) 框架.
- 通过考虑未知的真实数据分布来增强隐私保障.
- 减轻与传统的差异隐私相关的公用事业罚款.
主要方法:
- 使用Rényi分歧构建一个不确定性集,以获得强大的隐私.
- 采用强大的优化技术,将不确定性设置与多类型进行近似.
- 开发基于现有的局部差异隐私 (LDP) 机制的低复杂度算法,以实现可扩展性.
主要成果:
- 证明RLDP机制提供了强大的隐私保证.
- 实现高实用性,接近最佳水平,同时保持隐私.
- 数字实验验证拟议机制的有效性.
结论:
- RLDP框架在隐私和实用性之间提供了卓越的平衡.
- 对分布变化的稳定性是防止隐私泄露的关键.
- 开发的低复杂度算法使RLDP对大型数据集实用.
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