Abstract
The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Highlights
Data Mining is the computational process of discovering patterns in large data set involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems
When the data becomes massive in volume, many problems strike for security and privacy breach
The requirement of not losing the essence of data and still publishing it with the actual information is a challenge. Such troubles prompted the advancement of Privacy Preserving Data Mining (PPDM) Techniques
Summary
Received 10 Sep 2017, Revised 19th Sep 2017, Accepted 15th Oct 2017, Online 30th Oct 2017 Abstract— Data Mining has been the most researched area for researchers because of the possibilities of extension at each application of it. When the data becomes massive in volume, many problems strike for security and privacy breach. It is a timely need to secure the data while handling them to the known or unknown users. The requirement of not losing the essence of data and still publishing it with the actual information is a challenge. Such troubles prompted the advancement of Privacy Preserving Data Mining (PPDM) Techniques. Privacy Preserving has become an important issue in the development progress of Data Mining techniques. Methods like k-Anonymity, l-Diversity have been explored well by researchers but still, there are holes that force us to develop a more effective method and using such approach one can get better accuracy with minimum loss of data
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