Practical Privacy-Preserving Map Reduce Based K-means Clustering over Large-scale Dataset

Practical Privacy-Preserving Map Reduce Based K-means Clustering over Large-scale Dataset is a project report that focuses on the importance of preserving the privacy of the large-scale dataset easily. The handling of a large set of datasets in the cloud computing environment is trending. Cloud computing can offer reliable services with performance guaranteed. The large set of datasets may contain sensitive information that must be preserved. It is easily possible with the use of Map-reduce-based K-means clustering. Secure integration of Map-reduce can help in preserving the privacy of a large set of data. The synopsis on mini project report on practical privacy-preserving map reduce based k-means clustering over large-scale dataset is available. The users can free download abstract on pdf to understand the effects of practical privacy-preserving map reduce based k-means clustering over large-scale dataset.

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Topics Covered:
01)Introduction
02)Objectives, ER Diagram
03)Flow Chats, Algorithms used
04)System Requirements
05)Project Screenshots
06)Conclusion, References