International Journal of Electrical and Computer Engineering (IJECE) Vol. 8, No. 5, October 2018, pp. 3976~3983 ISSN: 2088-8708, DOI: 10.11591/ijece.v8i5.pp3976-3983  3976 Journal homepage: http://iaescore.com/journals/index.php/IJECE A Study on Big Data Privacy Protection Models using Data Masking Methods Archana R. A. 1 , Ravindra S. Hegadi 2 , Manjunath T. N. 3 1 R& D Centre, Bharathiar University, Coimbatore, Tamil Nadu, India 2 School of Compuational Sciences, Solapur University, Maharastra, India 3 Dept of ISE, BMS Institute of Technology, Bangalore, Karnataka, India Article Info ABSTRACT Article history: Received Jan 26, 2018 Revised Apr 20, 2018 Accepted Jul 2, 2018 In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications. Keyword: Big data privacy Business domains Data masking Dynamic data masking Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Archana R. A., R&D Centre, Bharathiar University, Coimbatore, Tamil Nadu, India. Email: archana.tnm@gmail.com 1. INTRODUCTION Big Data is growing from systems around us at faster rate, every second enormous amount of data is getting generated, and these data has characteristics of volume, variety and velocity. There is a need of big data management with respect to big data integration, Big data governance and quality of Big Data Privacy. In this connection data development and expansion, associations have poor perceivability into the area and utilization of their sensitive information. However security laws and directions require an exact comprehension of information hazard in view of different application domains and use crosswise over different frameworks [1], [2]. Enormous Data Privacy finds and arranges information to drive an exhaustive 360-degree perspective of the data for different purposes so you can group sensitive information with 360- degree perceivability De-distinguishes information so it can be securely utilized as a part of improvement and creation conditions. This ensures consistence with corporate approaches and industry directions data at the undertaking level, as a common administration [3]. The big data privacy framework provides a common infrastructure for development, testing and support, enabling scalability and repeatability across applications. We achieve reuse and scalability via the