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This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.<\/p>","DOI":"10.4018\/ijwp.2019070102","type":"journal-article","created":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T16:05:29Z","timestamp":1571760329000},"page":"14-40","source":"Crossref","is-referenced-by-count":1,"title":["Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization"],"prefix":"10.4018","volume":"11","author":[{"family":"Stephen Dass A.","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology, Vellore, India"}]},{"family":"Prabhu J.","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology, Vellore, India"}]}],"member":"2432","reference":[{"key":"IJWP.2019070102-0","doi-asserted-by":"crossref","unstructured":"Al-Zobbi, M., Shahrestani, S., & Ruan, C. (2017, August). 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