Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories
To propose a secure framework for outsourced privacy-preserving storage and retrieval in large shared image repositories.
Our proposal is based on IES-CBIR, a novel Image Encryption Scheme that exhibits Content-Based Image Retrieval properties. The framework enables both encrypted storage and searching using Content-Based Image Retrieval queries. Images are outsourced to repositories that reside in the cloud. Each repository is used by multiples Users, where they can both add their own images and/or search using a query image. Each repository is created by a single user. Upon the creation of a repository, a new repository key is generated by that user and then shared with other trusted users, allowing them to search in the repository and add/update images. In this work, we use the Bag-Of-Visual-Words (BOVW) representation to build a vocabulary tree and an inverted list index for each repository. We choose this approach for indexing as it shows good search performance and scalability properties. In the BOVW model, feature-vectors are hierarchically clustered into a vocabulary tree (also known as codebook), where each node denotes a representative feature-vector in the collection and leaf nodes are selected as the most representative nodes (called visual words).