The principle of biometric fusion, which entails combining multiple biometric matchers, is often used to (a) improve recognition accuracy and (b) increase the security of biometric systems. However, fusion can expose information generated by individual biometric matchers that an adversary can exploit. This paper explores the possibility of performing score-level and decision-level fusion by utilizing fully homomorphic encryption (FHE) for enhanced security and privacy. In the context of decision-level and score-level fusion, we appropriate a comparison algorithm that can operate on fully homomorphically encrypted inputs. Furthermore, for score-level fusion, we perform score normalization in the encrypted domain, thereby enhancing the privacy and security of the score data. Experiments on the NIST BSSR1 dataset suggest that FHE can provide a viable solution for securing biometric scores and decision data while retaining their utility in fusion. The contributions of this paper are as follows: (a) leveraging and implementing FHE-compatible operations in a biometric identification framework; and (b) evaluating the performance of such a framework on a real-world dataset.