NDLI: SecSIFT: Secure Image SIFT Feature Extraction in Cloud Computing

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Issue 1s(Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications and Special Issue on Extended Best Papers from ACM Multimedia 2014), October 2015

Issue 1, August 2015

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 11

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 10

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 9

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 8

SecSIFT: Secure Image SIFT Feature Extraction in Cloud Computing

Content Provider	ACM Digital Library
Author	Wang, Cong Yan, Jingbo Ren, Kui Qin, Zhan Chen, Chang Wen
Copyright Year	2016
Description	Author Affiliation: City University of Hong Kong, Kowloon, Hong Kong(Chen, Chang Wen; Ren, Kui; Yan, Jingbo; State university of new york at buffalo, Buffalo, New York (Qin, Zhan; Wang, Cong))
Abstract	The image and multimedia data produced by individuals and enterprises is increasing every day. Motivated by the advances in cloud computing, there is a growing need to outsource such computational intensive image feature detection tasks to cloud for its economic computing resources and on-demand ubiquitous access. However, the concerns over the effective protection of private image and multimedia data when outsourcing it to cloud platform become the major barrier that impedes the further implementation of cloud computing techniques over massive amount of image and multimedia data. To address this fundamental challenge, we study the state-of-the-art image feature detection algorithms and focus on Scalar Invariant Feature Transform (SIFT), which is one of the most important local feature detection algorithms and has been broadly employed in different areas, including object recognition, image matching, robotic mapping, and so on. We analyze and model the privacy requirements in outsourcing SIFT computation and propose Secure Scalar Invariant Feature Transform (SecSIFT), a high-performance privacy-preserving SIFT feature detection system. In contrast to previous works, the proposed design is not restricted by the efficiency limitations of current homomorphic encryption scheme. In our design, we decompose and distribute the computation procedures of the original SIFT algorithm to a set of independent, co-operative cloud servers and keep the outsourced computation procedures as simple as possible to avoid utilizing a computationally expensive homomorphic encryption scheme. The proposed SecSIFT enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.
Starting Page	1
Ending Page	24
Page Count	24
File Format	PDF
ISSN	15516857
e-ISSN	15516865
DOI	10.1145/2978574
Volume Number	12
Issue Number	4s
Journal	ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image feature detection SIFT Cloud computing Privacy-preserving
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Computer Networks and Communications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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SecSIFT: Secure Image SIFT Feature Extraction in Cloud Computing

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