SecHOG: Privacy-Preserving Outsourcing Computation of Histogram of Oriented Gradients in the Cloud

Abundant multimedia data generated in our daily life has intrigued a variety of very important and useful real-world applications such as object detection and recognition etc. Accompany with these applications, many popular feature descriptors have been developed, e.g., SIFT, SURF and HOG. Manipulating massive multimedia data locally, however, is a storage and computation intensive task, especially for resource-constrained clients. In this work, we focus on exploring how to securely outsource the famous feature extraction algorithm--Histogram of Oriented Gradients (HOG) to untrusted cloud servers, without revealing the data owner's private information. For the first time, we investigate this secure outsourcing computation problem under two different models and accordingly propose two novel privacy-preserving HOG outsourcing protocols, by efficiently encrypting image data by somewhat homomorphic encryption (SHE) integrated with single-instruction multiple-data (SIMD), designing a new batched secure comparison protocol, and carefully redesigning every step of HOG to adapt it to the ciphertext domain. Explicit Security and effectiveness analysis are presented to show that our protocols are practically-secure and can approximate well the performance of the original HOG executed in the plaintext domain. Our extensive experimental evaluations further demonstrate that our solutions achieve high efficiency and perform comparably to the original HOG when being applied to human detection.