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PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data

PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data QINGCHEN ZHANG and HUA ZHONG, Dalian University of Technology LAURENCE T. YANG, St. Francis Xavier University ZHIKUI CHEN and FANYU BU, Dalian University of Technology Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikuntanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Association for Computing Machinery

PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/2886779
Publisher site
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Abstract

PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data QINGCHEN ZHANG and HUA ZHONG, Dalian University of Technology LAURENCE T. YANG, St. Francis Xavier University ZHIKUI CHEN and FANYU BU, Dalian University of Technology Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikuntanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) – Association for Computing Machinery

Published: Oct 12, 2016

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