A View on providing anonymicity using top-down specialization on Cloud and Big Data by applying Parallelization

: 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 requirements. 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. Cloud computing provides massive computation power and storage capacity. Map reduce is a widely adopted parallel data processing framework, to address the scalability problem of the top-down specialization (TDS) approach for large-scale data 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.