不同的私有频段采矿 频段采矿
Shengzhi Xu1, Sen Su1, Xiang Cheng1
1Beijing University of Posts and Telecommunications, Beijing China.
概括
本研究介绍了PFS2,这是一种用于私人频序挖矿 (FSM) 的新算法. 通过采样有效地修剪候选序列,PFS2改善了隐私-实用性权衡,通过差异性隐私实现了准确的结果.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 保护隐私的技术 保护隐私的技术
背景情况:
- 频繁序列挖掘 (FSM) 对于在序列数据中发现模式至关重要.
- 在FSM中实现严格的差异隐私在平衡数据实用性和隐私方面存在挑战.
- 现有的方法在差异性私密的FSM中扎着噪声与候选人比率.
研究的目的:
- 开发一种新的差异私密频序挖掘 (FSM) 算法,PFS2.2.
- 通过有效地修剪候选序列,增强FSM中的公用事业-隐私权权衡.
- 在差异性隐私下引入对一般差距受限制的FSM的支持.
主要方法:
- 利用基于采样的候选修剪技术,使用样本数据库.
- 利用样本数据库上的杂局部支持估计来识别可能频繁的序列.
- 采用差距感知序列缩小,灵敏度计算和值放松以进行准确性和隐私校准.
主要成果:
- 已经正式证明PFS2是 ε-差异性私密的.
- 该算法有效地削减了不有前途的候选序列,显著改善了隐私-实用性权衡.
- 实验表明,在私下发现真实数据集上的频繁序列时,其准确度很高.
结论:
- PFS2是第一个在差异隐私中支持一般差距受约束的FSM的算法.
- 拟议的采样和修剪技术有效地减轻了私人FSM中的噪音影响.
- PFS2为准确和私密的频序发现提供了强大的解决方案.
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