Write some SA Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloudoftware

A large number of cloud services require users to share private data like electronic health records for data analysis or

mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity

is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications

increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to

capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing

anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of

scalability. In this paper, we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets

using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce

jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate

that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.

Compétences : Big Data Sales, Informatique en Nuage, Hadoop, Java, Ubuntu

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