SecHOG

Abstract

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 resourceconstrained 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.

Marble

“…They achieve this by constructing special polynomial approximations of well-known machine learning algorithms that are more suitable for FHE applications [22]. Wang et al also present a system to privately outsource feature-extraction to the cloud using FHE by redesigning the Histogram-of-Gradients calculation to be more suitable for FHE [40].…”

“…It is however less known that despite this slowdown, there are scenarios where FHE can be practical or near-practical. For instance, Wang et al show a system to privately outsource feature-extraction to the cloud using FHE [40]. Their solution outperforms local computation for reasonably sized images, even with the overhead of FHE.…”

Privacy‐preserving CNN feature extraction and retrieval over medical images

“…Histogram of Oriented Gradien (HOG) forms the feature descriptors of an image by computing the gradient direction histogram of the local region 27 . Wang et al 28 presented a secure outsourcing computing scheme based on HOG. The scheme can be divided into two cases: single‐server and dual‐server.…”