Cover
Vol. 18 No. 2 (2022)

Published: December 31, 2022

Pages: 60-68

Original Article

Privacy-Preserve Content-Based Image Retrieval Using Aggregated Local Features

Ali Lazim Lafta 1 Ayad I. Abdulsada
1Department of Computer science, Education College for Pure Sciences, University of Basrah, Basrah, 61004, Iraq
History
  • Received: April 10, 2022
  • Revised: May 21, 2022
  • Accepted: May 21, 2022
  • Available Online: July 2, 2022
DOI

https://doi.org/10.37917/ijeee.18.2.8

Abstract

Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.

Keywords

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