Multi-sample fusion with template protection

The widespread use of biometries and its increased popularity introduces privacy risks. In order to mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometries were introduced, also known as the field of template proteetion. Besides these developments, fusion of multiple sources of biometrie information have shown to improve the verification performance of the biometrie system. Our work eonsists of analyzing feature-level fusion in the context of the template proteetion framework using the helper-data system. We verify the results using the FRGC v2 database and two feature extraction algorithms

TOWARD THE SYSTEMATIZATION OF ACTIVE AUTHENTICATION RESEARCH

Authentication is the vital link between your real self and your digital self. As our digital selves become ever more powerful, the price of failing authentication grows. The most common authentication protocols are static data and employed only once at login. This allows for authentication to be spoofed just once to gain access to an entire user session. Behaviometric protocols continuously consume a user’s behavior as a token of authentication and can be applied throughout a session, thereby eliminating a fixed token to spoof. Research into these protocols as viable forms of authentication is relatively recent and is being conducted on a variety of data sources, features and classification schemes. This work proposes an extensible research framework to aid the systemization and preservation of research in this field by standardizing the interface for raw data collection, processing and interpretation. Specifically, this framework contributes transparent management of data collection and persistence, the presentation of past research in a highly configurable and extensible form, and the standardization of data forms to enhance innovative reuse and comparative analysis of prior research

Multi-Sample Fusion with Template Protection

Abstract: The widespread use of biometrics and its increased popularity introduces privacy risks. In order to mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometrics were introduced, also known as the field of template protection. Besides these developments, fusion of multiple sources of biometric information have shown to improve the verification performance of the biometric system. Our work consists of analyzing feature-level fusion in the context of the template protection framework using the helper-data system. We verify the results using the FRGC v2 database and two feature extraction algorithms.

Protection of privacy in biometric data

Biometrics is commonly used in many automated veri cation systems offering several advantages over traditional veri cation methods. Since biometric features are associated with individuals, their leakage will violate individuals\u27 privacy, which can cause serious and continued problems as the biometric data from a person are irreplaceable. To protect the biometric data containing privacy information, a number of privacy-preserving biometric schemes (PPBSs) have been developed over the last decade, but they have various drawbacks. The aim of this paper is to provide a comprehensive overview of the existing PPBSs and give guidance for future privacy-preserving biometric research. In particular, we explain the functional mechanisms of popular PPBSs and present the state-of-the-art privacy-preserving biometric methods based on these mechanisms. Furthermore, we discuss the drawbacks of the existing PPBSs and point out the challenges and future research directions in PPBSs

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones)