Multimedia Information Privacy Preservation with fusion of MapReduce, Fuzzy K-Means Clustering and Security for Cloud Storage  
  Authors : Sayyada Fahmeeda Sultana; Dr. Shubhangi D C


Multimedia data is expanding exponentially. The speedup growth of technology combined with storage capabilities and reasonable capacity has resulted in an explosion in multimedia availability and applications. Most data is available in the form of images and videos. Today a large amount of image data is produced through digital cameras, mobile phones and other sources. Processing this large set of images involves very complex and frequent operations in a large database that lead to challenges to improve query time and data storage capacity. Many image processing algorithms and computer vision are applicable to extensive data tasks. Mostly to run image processing algorithms on large data sets that are currently limited to the computing power of a single computer system. In order to handle such a huge data, cloud computing is used but storing data on cloud need security. Encrypting a complete image is a time consuming task to overcome the problem selective encryption is proposed based on MapReduce, Fuzzy K-Means Clustering and Data Encryption Standard(DES). The proposed scheme performs the feature extraction using MapReduce parallel speeding up the process ten times faster if ten Hadoop cluster nodes are involved. On the extracted features Fuzzy KMeans clustering is applied to segment the image and identify the region of interest(ROI). DES is applied on ROI to secure the image.


Published In : IJCAT Journal Volume 6, Issue 4

Date of Publication : April 2019

Pages : 21-26

Figures :02

Tables :--




Sayyada Fahmeeda Sultana : Department of Computer Science & Engineering, PDA College of Engineering, Gulbarga, India.

Dr. Shubhangi D C : Department of Computer Science, VTU PG Center, Gulbarga, India.








MapReduce, Image Feature Extraction, Mapper, Reducer, Fuzzy KMeans Clustering















A MapReduce based image Segmentation scheme is proposed that uses Fuzzy K-Means clustering to segment image into foreground and background. MapReduce perform feature extraction by first reading numerical equivalent of image as text input, then splits the input into blocks using Mapper process, the reducer process counts the number of different pixels values and return the sum of different pixels in each block with block number. This Reducer generated output gets as input to Fuzzy K-Means clustering and which gives two clusters, these are the foreground and background blocks of image. Then DES encryption algorithm is applied on foreground image. The proposed scheme is faster then executed on local machine with the utilization of parallel processes through the use of Hadoop cluster.










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