差异性隐私保护,用于持续发布实时位置数据
1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
保护持续的位置数据隐私至关重要. 这项研究引入了一种新的方法,即量化相关拉普拉斯机制 (QCLM),用于增强位置时间序列的差异隐私,平衡安全性和数据实用性.
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 网络安全 网络安全
背景情况:
- 在大数据时代,持续的实时位置数据至关重要.
- 在时间序列数据中保护位置隐私存在重大挑战,特别是考虑到数据相关性.
- 现有的差异隐私方法,如相关拉普拉斯机制 (CLM),在动态的,时间变化的环境中遇到困难.
研究的目的:
- 为了解决应用相关拉普拉斯机制 (CLM) 对连续位置数据发布的局限性.
- 开发一种保护隐私的方法,以考虑非静止位置数据的相关性.
- 为了减轻CLM中因动态环境中的短暂响应引起的输出偏差.
主要方法:
- 提出了基于静止位置增量来处理非静止数据的位置相关性估计方法.
- 引入了对CLM的自适应调整模型,称为量化相关拉普拉斯机制 (QCLM),使用参数量化.
- 开发了QCLM-Lowpass,这是一个利用位置数据的低通频谱特征来提高性能的实现.
主要成果:
- 提出的基于位置增量方法有效地估计了非静止位置数据的相关性.
- QCLM及其QCLM-Lowpass变体成功适应时间变化的环境,减少输出偏差.
- 模拟和现实世界的实验证实,QCLM-Lowpass在抵御相关性攻击和保持数据可用性之间提供了卓越的平衡.
结论:
- 开发的QCLM-Lowpass方法为保护隐私的持续位置数据发布提供了有效的解决方案.
- 这种方法增强了对相关时间序列数据的差异隐私,这对于大数据应用至关重要.
- 这些发现表明,在平衡位置隐私保护与数据实用性方面,有显著的改进.
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