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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Adaptive k-Anonymity Approach for Privacy Preserving in Cloud
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Adaptive k-Anonymity Approach for Privacy Preserving in Cloud

机译：云中隐私保护的自适应k-匿名方法

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摘要

Data privacy is very essential attribute for information sharing in cloud applications. Many end users find it challengingto adopt advanced technologies of cloud services, such as software-as-a-service, application-as-a-service, for protection ofsensitive data in their health applications. Considerable research has been done in Privacy Preservation of sensitive data while‘statistical analysis using data mining techniques’ such as k-anonymity being prominent. The term k-anonymity with respectto privacy of sensitive data is referred as sensitive information of an individual published and which cannot be distinguishedfrom at least k−1 individuals. k-Anonymity is achieved precisely with clustering techniques. However, the challenge is to findthe best seed values for collecting allied records which can be anonymized at the same level in order to reduce informationloss. This paper proposes a systematic approach for seed selection to cluster the records using the adaptive k-anonymityalgorithm. A comparative study of recent works with a goal to reduce information loss and execution time is calibrated.
机译：数据隐私是云应用程序中信息共享的非常重要的属性。许多最终用户发现采用云服务的高级技术（例如软件即服务，应用程序即服务）来保护其健康应用程序中的敏感数据具有挑战性。在敏感数据的隐私保护方面已经进行了大量研究，而“使用数据挖掘技术的统计分析”（例如k-匿名性）则很突出。关于敏感数据隐私的术语“ k-匿名性”是指已发布个人的敏感信息，并且不能与至少k-1个人区分开。 k-匿名性是通过聚类技术精确实现的。但是，挑战在于找到最佳的种子值来收集可在同一级别匿名化的关联记录，以减少信息损失。本文提出了一种系统的种子选择方法，利用自适应k-匿名算法对记录进行聚类。已对旨在减少信息丢失和缩短执行时间的近期作品进行了比较研究。

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