Secure SURF with Fully Homomorphic Encryption

TL;DR

This paper presents a framework for computing SURF features securely in the cloud using Fully Homomorphic Encryption, balancing privacy with computational accuracy.

Contribution

It introduces a rational number-based FHE-compatible method for SURF, including error bounds and empirical validation.

Findings

Accurately computes SURF keypoints in FHE for various image sizes.

Provides tight error bounds related to FHE parameters.

Demonstrates practical feasibility of secure SURF computation.

Abstract

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.