Efficient encrypted image retrieval in IoT-cloud with multi-user authentication

Abstract

Encryption is one of the best methods to safeguard the security and privacy of an image. However, looking through encrypted data is difficult. A number of techniques for searching encrypted data have been devised. However, certain security solutions may not be used in smart devices in IoT-cloud because such solutions are not lightweight. In this article, we present a lightweight scheme that can enable a content-based search through encrypted images. In particular, images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two feature vectors. In addition, we use a hashing method, namely, locality-sensitive hashing, to devise the searchable index. The use of an locality-sensitive hashing index increases the proficiency and effectiveness of a system, thereby allowing the retrieval of only relevant images with a minimum number of distance evaluations. Refining vector techniques are used to refine relevant results efficiently and securely. Our index construction process ensures that stored data and trapdoors are kept private. Our system also efficiently supports multiuser authentication by avoiding the expensive traditional method, which enables data owners to define who can search for a specific image. Compared with other similarity-based encryption methods predicated upon searchability, the option presented in this study offers superior search speed and storage efficiency.

Privacy preserving search index for image databases based on SURF and order preserving encryption

“…Some of these studies use aggregation of feature vectors as in [16] to improve the accuracy by gathering more information about an image. The encryption mechanism in [17, 18] is based on hashing functions, while secure ASPE is employed in [16], and OPE is used in our work.…”

“…Construction of the BOVW is done using various clustering methods. Authors of [17, 18] use the LSH technique in BOVW creation. On the other hand, in [16], a balanced binary clustering technique (BBC) is used to construct the index tree.…”

“…They used SIFT local features and local sensitive hashing (LSH) in pre‐filtering of images to increase efficiency. In [18], another secure CBIR system of multi‐users in IoT‐cloud is introduced. Their scheme is lightweight such that it can be employed in smart devices.…”

A new clothing image retrieval algorithm based on sketch component segmentation in mobile visual sensors

“…[1][2][3] Through these mobile sensors, users can connect directly to the Internet and conveniently store and collect massive image data from various sources. 4 Moreover, these special mobile sensors, for example, smart phones and tablets, have friendly human-computer interface where users can easily control the mobile sensors by touching or sketching on the screen, well suited even for who are not computer savvy. Therefore, sketching has become easy and ubiquitous, and we can sketch on different kinds of mobile sensors, such as sketching on the mobile phones and cameras even on the smart watches.…”

Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

“…On cloud server, the image search is performed, thus helps in reducing the cost for the end user. Another encrypted image retrieval system in IoT with multi-user authentication is proposed by [21]. This system is lightweight, enabling content-based search through decrypted images.…”