• Varsha Bais
• SayyadaFahmeeda Sultana

Big Data is term used to describe a collection of data that is huge in size and yet growing exponentially. Managing this big data with secure and privacy is major outline goal. Data mining is primarily done to extract and retrieve information. One of the major concern in big data mining approach is with security and privacy big data application such as online social media, mobile services , health care etc. Data sets in such application often contain privacy sensitive information which brings about privacy concerns potentially if the information is shared or released to third party .In this project anonymization technique is used via generalization to statisfy the given privacy model .the project proposed two phase clustering method. In first phase the dataset is collected and this dataset is partitioned into number of attributes this is done by t-ancestor clustering,and during this phase attributes are placed into three different identifiers those are General identifier, Sensitive identifier ,Quasi identifier .In second phase only sensitive identifiers are anonymized before sharing data. A variety of privacy models and data anonymization approaches has been proposed, however applying these traditional approach to big data anonymization poses scalability and efficiency challenges because of the “3Vs”, that is Volume, Velocity, Variety. Hence our approaches can preserve the proximity privacy, and can significantly improves the scalability and time-efficiency of local-recoding anonymization over existing approaches.

Big Data, Attributes, Data Anonymization, Local Recoding, Proximity Privacy .

"PRIVACY PRESERVATION FOR SCALABLE BIG DATA IN CLOUD ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.432-435, June 2019, Available :http://www.jetir.org/papers/JETIR1906S02.pdf

"PRIVACY PRESERVATION FOR SCALABLE BIG DATA IN CLOUD ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp432-435, June 2019, Available at : http://www.jetir.org/papers/JETIR1906S02.pdf