Secure SURF with Fully Homomorphic Encryption
This work addresses privacy and security concerns for users offloading image feature extraction to cloud services, though it is incremental as it adapts an existing method to FHE.
The paper tackled the problem of securely computing Speeded Up Robust Features (SURF) in the cloud by using Fully Homomorphic Encryption (FHE), resulting in a framework that accurately computes most SURF keypoints while providing tight error bounds for rational number conversion.
Cloud computing is an important part of today's world because offloading computations is a method to reduce costs. In this paper, we investigate computing the Speeded Up Robust Features (SURF) using Fully Homomorphic Encryption (FHE). Performing SURF in FHE enables a method to offload the computations while maintaining security and privacy of the original data. In support of this research, we developed a framework to compute SURF via a rational number based compatible with FHE. Although floating point (R) to rational numbers (Q) conversion introduces error, our research provides tight bounds on the magnitude of error in terms of parameters of FHE. We empirically verified the proposed method against a set of images at different sizes and showed that our framework accurately computes most of the SURF keypoints in FHE.