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

Abstract

Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset java project report Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. With the explosion of data in today’s big data era, a major trend to handle a clustering over large-scale datasets is outsourcing it to public cloud platforms. This is because cloud computing offers not only reliable services with performance guarantees, but also savings on in-house IT infrastructures.

H/W System Configuration:-

System          : Pentium I3 Processor.
Hard Disk       : 500 GB.
Monitor          : Standard LED Monitor
Input Devices : Keyboard
Ram               : 4 GB

S/W System Configuration:-

Operating system              : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database                          : MYSQL

Conclusion

In this work, we proposed a Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset java project report scheme in cloud computing. Thanks to our light-weight encryption design based on the LWE hard problem, our scheme achieves clustering speed and accuracy that are comparable to the K-means clustering without privacy protection.

Considering the support of large-scale dataset, we securely integrated MapReduce framework into our design, and make it extremely suitable for parallelized processing in cloud computing environment. In addition, the privacy preserving Euclidean distance comparison component proposed in our design can also be used as an independent tool for distance based applications.