Computational workload in biometric identification systems: an overview <s> Data Security <s> Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given. <s> BIB001 </s> Computational workload in biometric identification systems: an overview <s> Data Security <s> Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection. <s> BIB002 </s> Computational workload in biometric identification systems: an overview <s> Data Security <s> Fingerprint indexing has been extensively studied, and a number of approaches have been proposed in the literature. However, the vast majority of proposed approaches are based on original fingerprint templates without applying any protection mechanism. Secure fingerprint indexing algorithm has been rarely investigated. This paper presents a secure fingerprint indexing algorithm whose security is enhanced by a standard encryption algorithm. The proposed approach generates a binary template and creates an index space based on encrypted minutiae information. No original biometric information needs to be stored in the database, thus the adversary is unable to reverse the plain minutiae information without knowing the secret key. According to our experiments on both public datasets and a large-scale synthetic dataset, the proposed approach still maintains a very good performance in terms of low pre-selection error rate at small penetration rate. <s> BIB003 </s> Computational workload in biometric identification systems: an overview <s> Data Security <s> Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives. <s> BIB004 </s> Computational workload in biometric identification systems: an overview <s> Data Security <s> Protecting the privacy of the enrolled subjects is an important requirement expected from biometric systems. In recent years, numerous template protection schemes have been proposed, but so far none of them have been shown to be suitable for indexing (workload reduction) in the computationally expensive identification mode. This paper presents a, best to the authors' knowledge, first method in the scientific literature for indexing protected iris templates. It is based on applying random permutations to Iris-Code rows, and subsequent indexing using Bloom filters and binary search trees. In a security evaluation, the unlinkability, irreversibility and renewability of the method are demonstrated quantitatively. The biometric performance and workload reduction are assessed in an open-set identification scenario on the IITD and CASIA-Iris- Thousand datasets. The method exhibits high biometric performance and reduces the required computational workload to less than 5% of the baseline Iris-Code system. <s> BIB005
In addition to the need for computational workload reduction, which was the core topic of this article, the potential of data exposure is a large concern in biometric system deployments, where the stored data is, in most cases, secured using traditional encryption algorithms (see e.g. BIB002 ). From the technical point of view, this means that should the data be compromised, serious problems such as identity theft, cross-matching without consent arise, furthermore the renewability of such biometric templates is severely limited. Additionally, the centralised storage of sensitive personal and biometric data has increasingly been under scrutiny, both by the general public and various non-governmental organisations, which recently has led to widened legislation against privacy violations (e.g. GDPR in Europe  ). The ISO/IEC international standard on biometric information protection  stipulates several properties required for biometric template protection schemes. While many approaches have been proposed for normal biometric systems (see e.g. BIB001 for a survey), template protection coupled together with computational workload reduction has received relatively little attention in the scientific literature. Some early proof-of-concept works and trade-off analyses have been carried out e.g. in BIB003 BIB005 BIB004 .