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 multi-user 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.

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure ( PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.