Privacy-Preserving Argumentative Explanations (Student Abstract)

Abstract

We propose a framework for privacy-preserving argumentative explanations using homomorphic encryption. This method applies the Cheon-Kim-Kim-Song scheme, along with a soft k-means adapted for encrypted computation, to generate explanations without exposing sensitive data. By leveraging GPU acceleration, speedups of approximately 470–670 times were achieved compared with CPU execution. Experimental results show that explanation fidelity is maintained for small- to medium-scale models, whereas significant degradation occurs in larger models. These findings suggest that our study provides an initial step toward enabling secure and trustworthy argumentative explanations under encryption while also highlighting the challenges that remain for generalizability to more complex models.