Biometric Fuzzy Extractor Scheme for Iris Templates

In: The 2009 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP'09), The 2009 International Conference on Security and Management (SAM'09), Vol II, Proceedings 563--569. H.R. Arabnia and K. Daimi (Eds.), Las Vegas (USA), July, 2009Biometric recognition offers a reliable and natural solution to the problem of user authentication by means of her physical and behavioral traits. An iris template protection scheme which associates and retrieves a secret value with a high level of security, is proposed. The security is guaranteed thanks to the requirements of fuzzy extractors. The implementation of the scheme is done in Java and experimental results are performed to calculate its False Acceptance Rate and its False Rejection Rate.This work has been partially supported by Ministerio de Industria, Turismo y Comercio (Spain), in collaboration with CDTI and Telefónica I+D under the project SEGUR@ (CENIT-2007 2004).Peer reviewe

Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models

Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss. Our attacks are black-box, requiring no knowledge of the weights of the target neural network, only the dimension, and values of the output vector. To show the versatility of the alignment loss, we apply our attack framework to the task of membership inference (Shokri et al., IEEE S&P 2017) on biometric data. For the iris, membership inference attack against classification networks improves from 52% to 62% accuracy.Comment: This is a major revision of a paper titled "Inverting Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models" by the same authors that appears at IJCB 202

Iris Template Protection Based on Local Ranking

Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Security Efficiency Analysis of a Biometric Fuzzy Extractor for Iris Templates

A Biometric fuzzy extractor scheme for iris templates was recently presented in [3]. This fuzzy extractor binds a cryptographic key with the iris template of a user, allowing to recover such cryptographic key by authenticating the user by means of a new iris template from her. In this work, an analysis of the security eficiency of this fuzzy extractor is carried out by means of a study about the behavior of the scheme with cryptographic keys of diff erent bitlengths: 64, 128, 192, and 256. The diff erent sizes of the keys permit to analyze the variability of the intra- and inter-user in the iris templates.Peer reviewe