<p>Biometric cryptosystems enable privacy-preserving authentication using biometric data, such as fingerprints or iris scans. However, single modalities suffer from limited entropy, impacting both recognition performance and security. This work investigates the fusion of multiple biometric characteristics in a Deep Multi-biometric Fuzzy Commitment Scheme. In the experimental setup, we demonstrate how Deep Convolutional Neural Networks (DCNNs) are used to tackle the challenge of non-uniform representations by generating uniform embeddings. Uni-modal databases of iris and fingerprint embeddings, as well as the corresponding multi-biometric database, are employed for this purpose. Three fusion methods are proposed: concatenation, interleaving, and random shuffling within the fuzzy commitment scheme using error correction methods based on Hadamard and Reed-Solomon codes. The evaluation of performance and security reveals that random shuffling outperforms other methods like interleaving and concatenation in terms of recognition performance. Concatenation displayed the lowest performance. Finally, the findings are summarized and potential improvements are discussed.</p>