@Article{jbd.2020.01004,
AUTHOR = {Qiuju Ji, Peipeng Yu, Zhihua Xia},
TITLE = {QDCT Encoding-Based Retrieval for Encrypted JPEG  Images},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {33--51},
URL = {http://www.techscience.com/jbd/v2n1/40129},
ISSN = {2579-0056},
ABSTRACT = {Aprivacy-preserving search model for JPEG images is proposed in paper, which 
uses the bag-of-encrypted-words based on QDCT (Quaternion Discrete Cosine Transform) 
encoding. The JPEG image is obtained by a series of steps such as DCT (Discrete Cosine 
Transform) transformation, quantization, entropy coding, etc. In this paper, we firstly 
transform the images from spatial domain into quaternion domain. By analyzing the 
algebraic relationship between QDCT and DCT, a QDCT quantization table and QDTC 
coding for color images are proposed. Then the compressed image data is encrypted after 
the steps of block permutation, intra-block permutation, single table substitution and stream 
cipher. At last, the similarity between original image and query image can be measured by 
the Manhattan distance, which is calculated by two feature vectors with the model of bagof -words on the cloud server side. The outcome shows good performance in security attack 
and retrieval accuracy.},
DOI = {10.32604/jbd.2020.01004}
}