Biometrics is rapidly becoming the principal technology for automatic people authentication. The main advantage in using biometrics over traditional recognition approaches relies in the difficulty of losing, stealing, or copying individual behavioral or physical traits. The major weakness of biometrics-based systems relies in their security: in order to avoid data stealing or corruption, storing raw biometric data is not advised. The same problem occurs when biometric templates are employed, since they can be used to recover the original biometric data. We employ cryptographic techniques to protect dynamic signature features, making it impossible to derive the original biometrics from the stored templates, while maintaining good recognition performances. Together with protection, we also guarantee template cancellability and renewability. Moreover, the proposed authentication scheme is tailored to the signature variability of each user, thus obtaining a user adaptive system with enhanced performances with respect to a nonadaptive one. Experimental results show the effectiveness of our approach when compared to both traditional nonsecure classifiers and other, already proposed protection schemes.