Homomorphic Encryption-Based Secure SIFT for Privacy-Preserving Feature Extraction

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that Scale In variant Feature Transform (SIFT) has been widely adopted in various fields, this paper is the first to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all the operations in SIFT must be moved to the encrypted domain, the authors propose a homomorphic encryption-based secure SIFT method for privacy preserving feature extraction and representation based on Paillier cryptosystem.