• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Database and Expert Systems Applications; Lecture Notes in Computer Science; 4080 >Towards an Anti-inference (K, l)-Anonymity Model with Value Association Rules
【24h】

Towards an Anti-inference (K, l)-Anonymity Model with Value Association Rules

机译：具有价值关联规则的反推论（K，l）-匿名模型

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As a privacy-preserving microdata publication model, K-Anonymity has some application limits, such as (1) it cannot satisfy the individual-defined k mechanism requirement, and (2) it is attached with a certain extent potential privacy disclosure risk on published micro-data, i.e. existing high-probability inference violations under some prior knowledge on k-anonymized microdata that can surely result in personal private information disclosure. We propose the (k, l)-anonymity model with data generalization approach to support more flexible and anti-inference k-anonymization on a tabular microdata, where k indicates the anonymization level of an identifying attribute cluster and l refers to the diversity level of a sensitive attribute cluster on a record. Within the model, k and l are designed on each record and they can be defined subjectively by the corresponding individual. Beside, the model can prevent two kinds of inference attacks for microdata publication, (1) inferring identifying attributes values when their value domains are known; (2) inferring sensitive attributes values with respect to some value associations in the microdata. Further, we propose an algorithm to describe the k-anonymization process in the model. Finally, we take a scenario to illustrate its feasibility, flexibility, and generality.
机译：作为保护隐私的微数据发布模型，K-匿名性具有一些应用限制，例如（1）它不能满足个人定义的k机制要求，并且（2）在一定程度上附加了潜在的隐私公开风险微数据，即在先验知识下对k匿名化的微数据存在的现有高概率推理违规行为，肯定会导致个人私人信息泄露。我们提出一种（k，l）-匿名模型，该模型采用数据泛化方法来支持表格微数据上更灵活和反推论的k-匿名化，其中k表示识别属性集群的匿名化级别，l表示记录上的敏感属性簇。在模型内，在每个记录上设计k和l，并且可以由相应的人主观地定义它们。此外，该模型还可以防止针对微数据发布的两种推理攻击：（1）在已知属性值的值域时进行推断。 （2）推断有关微数据中某些值关联的敏感属性值。此外，我们提出了一种描述模型中k匿名化过程的算法。最后，我们以一个场景来说明其可行性，灵活性和通用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号