基于差异隐私的顺序模式的私人垂直挖掘,具有高效率和实用性的高效率和实用性.
Wenjuan Liang1,2, Wenke Zhang1,2, Songtao Liang3
1College of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
Scientific reports
|October 19, 2023
概括
本研究介绍了privertical,这是一种用于私人顺序模式挖矿的新方法. 它通过将垂直采矿与差异性隐私相结合来提高隐私和效率,从而提高数据分析的准确性.
科学领域:
- 数据挖掘和保护隐私的技术
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 序列模式挖掘对于数据分析至关重要,但由于敏感的序列数据,它引发了隐私问题.
- 现有的差异隐私 (DP) 解决方案通常使用低效的水平挖矿算法,限制准确性和效率.
- 需要进行保护隐私的顺序数据分析,以平衡强大的隐私保证和高性能.
研究的目的:
- 提出一个新的私人顺序模式采矿方案,privVertical,将垂直采矿与差异隐私相结合.
- 为了提高保护隐私的顺序模式采矿的效率和准确性.
- 确保拟议的方法符合严格的隐私标准.
主要方法:
- 开发了privertical,这是一个集成垂直采矿算法与差异隐私的方案.
- 引入了一个差异化的私有HashMap (privHashMap),使用Sparse Vector技术记录杂的支持计数.
- 实施了不常见候选序列的预修剪,以减少噪音和提高准确性.
主要成果:
- PrivVertical避免了昂贵的数据库扫描和横向方法固有的预测.
- privHashMap和Sparse Vector技术提高了频繁项目记录的准确性.
- 实验表明,privertical在相当的隐私级别下实现了更高的准确性和效率.
- 理论分析证实 privVertical 满足了 ε-差异性隐私.
结论:
- PrivVertical提供了一种高效准确的解决方案,用于保护隐私的顺序模式挖矿.
- 拟议的方法有效地解决了基于水平采矿的现有DP解决方案的局限性.
- 这种方法使敏感的顺序数据分析具有强有力的隐私保证.
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