Reliable location selection and hierarchical interleaved bloom filter based iris template protection

A cancellable iris template protection scheme based on inverse merger and Bloom filter

Iris biometric is the most common and widely accepted biometric authentication method due to its high accuracy. The iris biometric template should be protected to overcome security and privacy attacks. The two standard iris biometric template protection methods are bloom filter and double bloom filter‐based feature transformations. In this article, we first permuted and then applied the p‐order Hadamard product on the iris template before the bloom filter and double bloom filter‐based transformation to enhance the security. The performance and security are measured using the CASIAv1 iris benchmark database to study the effect of Hadamard multiplication on bloom and double bloom filter‐based transformation. The Hadamard multiplication on the iris template increases the security of the transformed iris template and slightly degrades the matching performance.

In iris biometric recognition systems, protecting the storage and transmission of iris templates is crucial, and template protection techniques are pivotal for ensuring their security. A prevalent approach involves using indexing methods as an effective algorithm for iris template protection, leveraging the index or rank of the extracted iris code to generate a secure iris template. Meantime, many privacy threats to biometric data have emerged, necessitating heightened protection measures. Specifically, protecting the privacy of iris data is imperative within the context of iris template protection during recognition processes. As stipulated by the international standard ISO/IEC 30136, effective iris template protection must concurrently meet the criteria of irreversibility, revocability, and unlinkability. Nevertheless, existing indexing methods on iris template protection faced the formidable challenge of simultaneously fulfilling these three privacy requirements while maintaining the efficacy of iris recognition. This paper introduces a standard ranking (standardR) algorithm, named standardR, designed to enhance the security of iris templates by transforming each iris template into an irreversible representation. The experimental results on the benchmarked Casia-Iris-interval dataset, along with two additional iris datasets MMU1 and UBRIS 1, demonstrate the efficacy of the proposed algorithm. The proposed standardR algorithm achieves an equal error rate (EER) of 0.1695% and an area under the curve of 0.93011% with the Casia-Iris-Interval dataset. Furthermore, the algorithm maintains efficient recognition with a reduced iris code length of 1280 bits, a time complexity of O(n log n), and satisfies the biometric template protection (BTP) requirements in irreversibility, unlinkability, and renewability.

Iris template protection using double bloom filter based feature transformation

Security analysis and improvement of some biometric protected templates based on Bloom filters

In the 21st century, deployment of large biometric systems worldwide like Aadhaar (India), eKTP (Indonesia), and MyKad (Malaysia) surges the immediate need to secure biometric systems due to their vulnerabilities to several security threats. Unlike conventional password-based systems, biometric templates, if compromised, cannot be revoked. In such situations, biometric cryptosystems and cancelable biometrics (CB) come as a rescue measure. Specifically, in the case of CB; biometric template is encoded with a distortion scheme or with a non-invertible transformation that varies for each underlying application. The concept of cancelability in biometric authentication was developed to address three non-functional requirements, i.e., revocability, non-invertibility, and unlinkability simultaneously along with functional requirement, i.e., performance. In the past decade, most of the work has been carried out to secure biometric templates, particularly for iris and fingerprints, since these are the two commonly used biometric traits for identification purposes. Apart from that, few work have been carried out on studying cancelability for multimodal biometrics and finger-veins. The fundamental issue with most of these techniques is that they are tested on a small dataset, which is mainly collected in a controlled environment. Thus, the generalisability of these techniques is questionable in the case of large-scale databases, which is indeed needed. Moreover, in the present-day scenario, attackers are getting smarter day by day, and thus in the view of new-attacks, security of already proposed techniques is quite vulnerable. An in-depth security analysis of the proposed methods is a must. As we know, there is an inexorable trade-off between security and performance; thus, one should be very careful while deciding it depending upon the application in hand. Another point of concern is that most of the techniques are applicable only on one biometric trait and fail on others except a few like BioHashing and Bloom filter which are applied simultaneously on the face, iris, and fingerprints. Since nowadays multimodal biometrics are often used, it is desirable that the same template protection technique is equally applicable with good performance to all biometric traits. Here, in this chapter, our focus will be towards illustrating different template protection techniques while keeping in mind all the above-raised issues. Moreover, the past ten years has seen a lot of research work based on deep learning techniques. Thus, our primary focus in this chapter will be to explore how the latest AI techniques, particularly deep learning-based, can shape the future of template protection. We will discuss in detail the limitations and advantages of using deep learning-based methods in the template protection domain. In addition to this, we will also suggest some future directives in the domain of template protection using cutting-edge deep learning techniques.

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PDF

Biometric system is currently being used in numerous large-scale projects and applications. Multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification and is more secure than unimodal biometric authentication. Though multi-biometric authentication systems are more secure, private biometric data of the user may get compromised which causes security threats. Also with the additions of new biometric traits size of database grows. Any information leakage may cause critical privacy and security issues. This paper proposes multi-biometric template protection technique based on hybridization of feature transformation and image transformation. Discrete Cosine Transform (DCT) is used for feature transformation and polynomial based Secret Sharing scheme (SSS) are used for hybridization which results in protection of multi-biometric template. The proposed system uses iris template and fingerprint template for multi-biometric authentication. 500 traits of iris template and fingerprint template of 100 individuals are used to evaluate the proposed technique. Performance measure such as False Rejection Rate (FRR) and Genuine Acceptance Ratio (GAR) are used to measure accuracy of implemented method. The obtained high average value 96.07% of GAR and low average value 3.92% of FRR proves high accuracy of the proposed multi-biometric technique along with template protection due to fusion. The original 512 × 512 size biometric template is reduced to vector of size 16 × 16 in feature transformation and vector of size 16 × 8 in image transformation phase. The image transformation provides protection to database by decentralization of database. The proposed technique is a secure system which provides template protection, high accuracy, reduced size database and avoidance of single point failure.

Biometric technologies are being prominently used everywhere. However, the leakage of biometric information can pose a serious security risk, making the protection of biometric templates particularly important and receiving more attention. Rathgeb et al. first proposed the cancelable biometric technology based on Bloom filters in 2013, which has been applied to protect different biometric templates. Bloom filter-based biometrics offer the advantages of alignment-free, fast recognition and high accuracy. An ideal biometric system should also be irreversibility and unlinkability. In this paper, firstly, we propose a reverse reconstruction attack. Through the reverse reconstruction of Bloom filters, we find that the reconstructed biometric data and the original biometric data have some strong statistical correlation, which proves that the scheme has the linkability defect. Experiments show that for the original Bloom filter-based biometric template protection scheme, we can judge whether two different biometric templates belong to the same user with a success probability of 71.0%. Secondly, to remedy above defect, we construct a structure-preserving encryption scheme, i.e., the feature template encrypted with it maintains the structure and length of the original template, making it impossible for an attacker to reconstruct meaningful data from Bloom filter. Finally, an improved biometric template protection scheme based on Bloom filters is proposed by introducing the proposed encryption. Attack experiment shows that the improved scheme can effectively resist the reverse reconstruction attack, with the success probability of attacking the unlinkability of the improved scheme being 50.0%, which is the same as the probability of random guessing. Performance evaluation shows that the proposed scheme maintains the biometric performance of the original system and the unprotected system.

This paper explores the recently published works on iris template protection namely Indexing-First-One hashing. Despite the Indexing-First-One hashing offers high recognition performance with resistant against several major privacy and security attacks, it does not resolve the rotation inconsistent issues existed in conventional iris template due to head tilt/ rotation during user's eyes images acquisition. Hence, a pre-alignment step is required for the conventional IFO hashed code matching. Consequently, this increased the computational cost heavily. Hereby, we address the rotation inconsistent issue by proposing an alignment-free IFO hashing through a pre-transformation based on Bloom filter generation. The proposed pre-alignment IFO hashing shows promising recognition performance, and the pre-alignment procedure is eliminated to lower computational cost.

In recent years, we have seen huge growth of biometric systems incorporated in devices such as smartphones and security is one of the major concerns. In this work a multi-biometric template protected system is proposed, based on Bloom filers and binarized statistical image features (BSIF). Features are extracted from face and both periocular regions and templates protected using Bloom filters. Score level fusion is applied to increase recognition accuracy. The system is tested on a database, consisting of 94 subjects, of images collected with smart phones. A comparison between unprotected and protected templates in the system shows the feasibility of the template protection method with observed Genuine-Match-Rate (GMR) of 95.95% for unprotected templates and 91.61% at a False-Match-Rate (FMR) of 0.01%. Irreversibility and unlinkability of the system is analysed based on a recently published security evaluation framework.

Multi-biometric template protection based on bloom filters

A Hybrid Ranking Algorithm for Secure and Efficient Iris Template Protection

As one of biometric technologies for personal identification and verification, many algorithm for iris recognition are proposed in recent decades. In these algorithms, iris templates are stored uncovered in the database. Once the database is compromised, the attackers can potentially obtain sufficient information to impersonate the users. This paper proposes a novel template protection algorithm for iris recognition, which stores the hash value instead of the iris template. Common vector method and non-uniform quantization method are used to eliminate the contradiction between the fuzzy character of iris information and the sensitive of the hash function. Experiments show the performance of the iris recognition system with template protection is improved.

Iris recognition system has demonstrated its strong capability in performing personal verification and identification with promising recognition accuracy. However, the conventional iris recognition system stores the unprotected iris templates in a database, which is potentially being compromised. Even though biometric template protection provides a feasible solution to secure biometric template, a trade-off between security and recognition accuracy is incurred. That is, the higher security level always trades with poor recognition accuracy and vice versa. In this paper, a new iris template protection scheme is proposed, namely “Indexing-First-One” (IFO) hashing. IFO hashing transforms the binary feature into index value with Jacaard distance preservation. The resultant template offers a good indication of inheriting similarity from the IrisCode and strong concealment of IrisCode against inversion attack as well as other major security and privacy attacks. Experiments on CASIA-v3 data set substantiate that the proposed scheme can achieve as low as 0.54 % equal error rate and well preservation of recognition performance before and after IFO hashing.