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PPHOCFS

PPHOCFS 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-Vaikun-tanathan (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 evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Association for Computing Machinery

PPHOCFS

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 ACM
ISSN
1551-6857
eISSN
1551-6865
DOI
10.1145/2886779
Publisher site
See Article on Publisher Site

Abstract

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-Vaikun-tanathan (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 evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering.

Journal

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

Published: Oct 12, 2016

Keywords: BGV encryption

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