A View on providing anonymicity using top - down specialization on Cloud and Big Data by applying Parallelization
Journal Title: International Journal of Scientific Research and Management - Year 2014, Vol 2, Issue 12
Abstract
A large number of cloud services require users to share private data for data analysis or mining, bringing privacy concerns. So anonymizing of data sets via generalization is done to satisfy certain privacy requirement s. 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 so ftware tools to capture, manage, and process such large - scale data within a tolerabl e elapsed time. Cloud computing provides massive computation power and storage capacity. Map reduce is a widely adopted parallel data processing framework, to address the sca lability problem of the top - down specialization (TDS) approach for large - scale dat a anonymization. This paper provides an overview of cloud and Big Data. It describes how these concepts are used in providing anonymicity. It also compares the top - down and bottom - up specialization. We close by are sharing our opinions on what some of the important open questions are in this area as well as our thoughts on how the anonymicity algorithms can be improvised so that might best seek out answers.
Authors and Affiliations
Prateek T R
Keywords
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