SEPIM: Secure and Efficient Private Image Matching

Matching a particular image within extensive datasets has become increasingly pressing in many practical fields. Hence, a number of matching methods have been developed when confidential images are used in image matching between a pair of security agencies, but they are limited by either search cost or search precision. In this paper, we propose a privacy-preserving private image matching scheme between two parties where images are confidential, namely secure and efficient private image matching (SEPIM). The descriptor set of the queried party needs to be generated and encrypted properly with the use of a secret key at the queried party side before being transferred to the other party. We present the development and validation of a secure scheme to measure the cosine similarity between two descriptor sets. To hasten the search process, we construct a tree-based index structure by utilizing the k-means clustering algorithm. The method can work without using any image encryption, sharing, and trusted third party. SEPIM is relatively efficient when set against other methods of searching images over plaintexts, and shows a higher search cost of just 14% and reduction in search precision of just 2%. We conducted several empirical analyses on real image collections to demonstrate the performance of our work.