Template protection is an issue of paramount importance for the design of secure and privacy-compliant biometric recognition systems. Template unlinkability, together with template irreversibility, is an essential requirement to properly guarantee template protection. In fact, it ensures that templates generated from the same trait, but used in different applications, cannot be linked to the same identity. This paper deals with the design of a system satisfying the unlinkability requirement. The robustness of the proposed solution is evaluated by exploiting methods stemming from the theory of stochastic optimization, as well as by using quantitative measures specifically proposed to characterize the unlinkability of biometric protection schemes. A case study using finger-vein biometrics is considered to test the proposed cryptosystem on non-ideal data. The proposed scheme guarantees 128 bits of security with acceptable false recognition rates in real-life conditions. Moreover, we provide guidelines to determine the parameters of the transformations to be applied to real biometric traits so as to ensure proper recognition, security, and unlinkability performance.

In this paper, we present a novel biometric cryptosystem obtaining perfect security, that is not leaking any information about the employed secret key from the knowledge of the stored helper data. While similar purposes have already been sought in the literature, the approaches proposed so far have been evaluated in terms of recognition performance under the unrealistic assumption of ideal statistical distributions for the considered biometric data. Conversely, in this paper, we investigate the applicability of the proposed framework to practical scenarios while managing a trade-off between privacy and recognition performance. This goal has been achieved by introducing a class of transformation functions enforcing zero-leakage secrecy, by designing an adaptive strategy for embedding the secret key bits into the selected features, and by developing a system parameters optimization strategy with respect to security, recognition performance, and privacy. Experimental tests conducted on real fingerprint data prove the effectiveness of the proposed scheme.

Biometric recognition systems, despite the advantages provided with respect to traditional authentication methods, have some peculiar weaknesses which may allow an attacker being falsely recognized or accessing users' personal data. Among such vulnerabilities, in this paper, we speculate on the hill-climbing attack, i.e., the possibility for an attacker to exploit the scores produced by the matcher with the goal of generating synthetic biometric data, which could allow a false acceptance. More in detail, we focus on multibiometrics systems and investigate about the robustness of different system architectures, both parallel and serial fusion schemes, against the hill-climbing attack. Nonuniform quantization is also evaluated as a possible countermeasure for limiting the effectiveness of the considered attacks in terms of recognition success rate and average number of required attempts without affecting the recognition performance.