Secure Fast Fourier Transform using Fully Homomorphic Encryption
This addresses privacy concerns in signal processing for users needing encrypted computations, but it is incremental as it applies existing FHE techniques to FFT.
The paper tackles the problem of performing secure signal processing by proposing a method to compute Fast Fourier Transform (FFT) using Fully Homomorphic Encryption (FHE), enabling privacy-preserving operations with bounded error and verification on 1D and 2D signals.
Secure signal processing is becoming a de facto model for preserving privacy. We propose a model based on the Fully Homomorphic Encryption (FHE) technique to mitigate security breaches. Our framework provides a method to perform a Fast Fourier Transform (FFT) on a user-specified signal. Using encryption of individual binary values and FHE operations over addition and multiplication, we enable a user to perform the FFT in a fixed point fractional representation in binary. Our approach bounds the error of the implementation to enable user-selectable parameters based on the specific application. We verified our framework against test cases for one dimensional signals and images (two dimensional signals).