@Article{jbd.2021.015892,
AUTHOR = {Jian Tang, Zhihua Xia, Lan Wang, Chengsheng Yuan, Xueli Zhao},
TITLE = {OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme  with Local Histograms in Cloud Environment},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {21--33},
URL = {http://www.techscience.com/jbd/v3n1/41298},
ISSN = {2579-0056},
ABSTRACT = {As the wide application of imaging technology, the number of big 
image data which may containing private information is growing fast. Due to 
insufficient computing power and storage space for local server device, many 
people hand over these images to cloud servers for management. But actually, it 
is unsafe to store the images to the cloud, so encryption becomes a necessary step 
before uploading to reduce the risk of privacy leakage. However, it is not 
conducive to the efficient application of image, especially in the Content-Based 
Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We design a set of JPEG format-compatible 
encryption method, making no file expansion to JPEG files. We firstly combine 
multiple adjacent 8 × 8 DCT coefficient blocks into big-blocks. Then, random 
scrambling and stream encryption are used on the binary code of DCT coefficients 
to protect the JPEG image privacy. The task of extracting features from encrypted 
images and retrieving similar images are done by the cloud server. The group 
index histograms of DCT coefficients are extracted from the encrypted big-blocks, 
then the global vector is produced to represent the JPEG image with the aid of bagof-words (BOW) model. The security analysis and experimental results show that 
our proposed scheme has strong security and good retrieval performance.},
DOI = {10.32604/jbd.2021.015892}
}