设计一种新的方法,使用一个基于贪和信息理论的集群算法来匿名化微数据集.
Reza Ahmadi Khatir1, Habib Izadkhah1, Jafar Razmara1
1Department of Computer Science, University of Tabriz, Tabriz 5166616471, Iran.
本研究引入了一种用于数据匿名化的新集群算法,平衡隐私和实用性. 该方法确保了k-匿名性和t-密切性,保护数据集中的敏感信息.
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:
- 数据匿名化对隐私至关重要,但在隐私增强和数据实用性之间面临着权衡.
- K-匿名性是一种标准技术,但当敏感值缺乏多样性时,它很容易暴露属性.
- T-closeness提供强大的隐私保护,防止属性披露.
研究的目的:
- 提出一种基于集群的数据匿名化新算法.
- 通过满足k-匿名性和t-密切性限制,实现严格的隐私保护.
- 通过最小化属性修改来平衡隐私保护和数据实用性.
主要方法:
- 使用一个贪和信息理论的集群算法.
- 数据基于准识别器相似性和敏感属性多样性进行聚类.
- 集群被调整 (分割/合并) 以满足k-匿名性和t-接近性要求.
- 准标识符值被用集群中心值替换为匿名化.
主要成果:
- 拟议的算法成功实现了k-匿名性和t-接近性.
- 实验结果显示,对属性值的修改最小化,保持数据实用性.
- 该方法在来自Facebook,Twitter和Google+的微数据集上得到验证.
结论:
- 这种新的集群算法有效地提高了数据隐私,同时保持了数据实用性.
- 该方法为匿名化敏感数据提供了一个强大的解决方案,解决了传统k-匿名性的局限性.
- 这种方法提供了一种实用的方法,用于在社交媒体环境中保护隐私的数据发布.
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