Privacy Preservation Enriched MapReduce for Hadoop Based BigData Applications
Chhaya Suryabhan Dule, K. M. Rajasekharaiah · 2014
As per increase in the of various internet enabled services and cloud applications, the requirement of cloud infrastructure with enhanced facilities is increasing with very vast pace. Due to the increase in multiuser communication scenario on cloud infrastructure, the securities of datasets are also increasing drastically. Most of critical data on cloud is strictly required to be enriched with security and privacy preserved. Considering these requirements for huge data such as BigData, here in this paper an enhanced and optimized system called Privacy preservation Enriched MapReduce framework for Hadoop based BigData applications is proposed. In the proposed system four models to enrich overall anonymity of critical datasets has been developed. These models are privacy characterization model,anonymizer for datasets, dataset update and privacy preserved data management. In the proposed system the data owner possesses authority and interface to introduce various security levels for its data to make it privacy preserved and anonymous. The proposed model facilitates data users to retrieve datasets in its anonymized form which ultimately provides user task without publishing critical detail information about original data. This system would not only facilitate anonymity for datasets in cloud infrastructure but also optimize data recomputation by means of its partial data retaining capacity. Thus, the proposed system would bring optimization not only in terms of privacy preservation but also with enhanced resource utilization in BigData based applications.