期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
期号：ICMEET
页码：512
出版社：S&S Publications
摘要：A large number of cloud services require users to share private data like electronic health records fordata analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacyrequirements such as k-anonymity is a widely used category of privacy preserving techniques. It is a challengefor existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due totheir insufficiency of scalability. We propose a scalable Bottom up Generalization (BUG) approach to anonymizelarge-scale data sets using the MapReduce framework on cloud. We deliberately design a group of innovativeMapReduce jobs to concretely accomplish the computation in a highly scalable way. We will try to prove that with thisapproach, the scalability and efficiency of BUG can be significantly improved over existing approaches.
关键词：Cloud; Data Anonymization; MapReduce; Bottom-up Generalization
