Privacy-preserving geo-tagged image search in edge–cloud computing for IoT

The Internet of Things (IoT) generates a significant volume of geo-tagged images via surveillance sensors in edge–cloud computing environments. Image search is essential to facilitate information sharing, data analysis, and strategic decision-making. However, outsourced images are typically encrypted for privacy protection, posing a challenge in simultaneously searching for visual and geographical relevance on encrypted images. To address this, this paper proposes an edge intelligence empowered privacy-preserving top-$k$ geo-tagged image search scheme for IoT in edge–cloud computing. The scheme presents a novel single-to-multi searchable encryption method for geo-tagged images that enables multiple users to perform secure nearest neighbor queries on a data source. Additionally, an extended anchor-based position determination method and an inner product-based distance calculation method are designed to enable geo-tagged image similarity calculation on ciphertext. Finally, a secure pruning method is introduced to improve query performance. Experiments are conducted to verify the performance of the scheme in terms of high efficiency and accuracy of the search.