CryptoKANs+: Can KAN Be Just an MLP? Towards a Fast and Accurate Privacy-Preserving Machine Learning Solution
Omar Tahmi, Chamseddine Talhi, Hakima Ould‐Slimane · 2025
The proliferation of the Internet of Things (IoT), coupled with rapid advances in Neural Networks (NNs), has given rise to the Internet of Artificially Intelligent Things. While cloud-based solutions enable scalable intelligence, they also introduce serious privacy risks, particularly during NN inference on sensitive data. Homomorphic Encryption (HE) offers a promising solution by enabling computations directly on encrypted data without decryption. However, integrating NNs with HE remains challenging-especially in introducing nonlinearity-due to the limited support for non-linear operations in current word-wise HE schemes. Existing approaches typically rely on approximated Activation Functions (AFs), often at the cost of reduced accuracy or increased computational overhead. In this paper, we compare Kolmogorov-Arnold Networks (KANs) with their counterparts, the Multi-Layer Perceptrons (MLPs), in privacy-preserving settings. We first formally demonstrate that KANs are equivalent to MLPs without the initial fully connected linear transformation layer when using the same AFs and transformations. Then, we propose CryptoKAN+, which integrates a learnable quadratic AF applied in the form of a Fully Connected Quadratic Transformation (FCQT) layer. In this design, the subsequent transformation is absorbed into the quadratic AF, eliminating the need for a post-linear transformation. Extensive experiments on the MNIST and Fashion-MNIST datasets reveal that CryptoKAN+ consistently outperforms stateof-the-art privacy-preserving machine learning (PPML) solutions in both accuracy and efficiency. These findings underscore the potential of KAN-based architectures with quadratic AFs as FCQT layers, making CryptoKAN+ a compelling choice for real-world applications requiring secure, accurate, and efficient inference.