HTF:同质树框架,用于差分私有地发布大型地理空间数据集,具有自调结构高度
Sina Shaham1, Gabriel Ghinita2, Ritesh Ahuja1
1University of Southern California, USA.
概括
这项研究引入了一种新的方法,用于从位置数据中创建准确的差异性私密直方图. 该方法优化了数据分区以实现密度均性,改善了基于位置的服务的隐私和实用性.
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 地理空间分析是什么
背景情况:
- 移动应用中的位置数据在运输和医疗保健等各个领域广泛使用.
- 对位置数据的统计查询,例如识别用户热点,至关重要,但也引发了隐私问题.
- 现有的差异私密直方图方法通常会因为数据独立的噪声加值而牺牲准确性.
研究的目的:
- 开发一种新的数据依赖方法,用于生成准确的差异性私密直方图.
- 为了解决平衡隐私和位置数据准确性的现有方法的局限性.
- 在差异隐私下优化分区策略,以增强直方图的实用性.
主要方法:
- 提出了一个基于密度同质性的空间数据分区的数据结构.
- 引入了自调机制,以优化分区深度和隐私预算分配.
- 开发了一种针对大规模现实世界数据集的扩展方法的优化方法.
主要成果:
- 与现有技术相比,拟议的方法显著提高了差异性私密直方图的准确性.
- 在大规模,真实世界的位置数据集上表现出卓越的性能.
- 基于密度同质性的分区有效地平衡了隐私和实用性.
结论:
- 这种新的方法提供了一种更准确,更有效的方式来处理位置数据与差异性隐私.
- 这种方法提高了基于位置的服务的实用性,同时保护了用户的隐私.
- 这些发现为在移动应用程序中更强大的隐私保护数据分析铺平了道路.
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