A Practical Exercise in Adapting SIFT Using FHE Primitives
This work provides a practical guide for adapting algorithms to FHE, addressing domain-specific bottlenecks in secure computation for applications like image processing.
The paper tackled the challenge of implementing the Scale Invariant Feature Transform (SIFT) algorithm using Fully Homomorphic Encryption (FHE) by identifying limitations like the lack of standard comparison operators and proposing methods to adapt regular code, reduce multiplicative depth, and use deferred computations to avoid expensive encrypted operations.
An exercise in implementing Scale Invariant Feature Transform using CKKS Fully Homomorphic encryption quickly reveals some glaring limitations in the current FHE paradigm. These limitations include the lack of a standard comparison operator and certain operations that depend on it (like array max, histogram binning etc). We also observe that the existing solutions are either too low level or do not have proper abstractions to implement algorithms like SIFT. In this work, we demonstrate: 1. Methods of adapting regular code to the FHE setting. 2. Alternate implementations of standard algorithms (like array max, histogram binning, etc.) to reduce the multiplicative depth. 3. A novel method of using deferred computations to avoid performing expensive operations such as comparisons in the encrypted domain. Through this exercise, we hope this work acts as a practical guide on how one can adapt algorithms to FHE