Unterschrift statisch, dynamisch, auch Handschrift Zahnabdruck Realisierung und Funktionsweise[ Bearbeiten Quelltext bearbeiten ] Ein biometrisches Erkennungssystem setzt sich im Wesentlichen aus den Komponenten Sensor MesswertaufnehmerMerkmalsextraktion und Merkmalsvergleich zusammen.
Biometrics enables convenient authentication based on a person's physical or behavioral characteristics. In comparison with knowledge- or token-based methods, it links an identity directly to its owner. Furthermore, it can not be forgotten or handed over easily.
As biometric techniques have become more and more efficient and accurate, they are widely used in numerous areas. Among the most common application areas are physical and logical access controls, border control, authentication in banking applications and biometric identification in forensics.
In this growing field of biometric applications, concerns about privacy and security cannot be neglected. The advantages of biometrics can revert to the opposite easily.
The potential misuse of biometric information is not limited to the endangerment of user privacy, since biometric data potentially contain sensitive information like gender, race, state of health, etc. Different applications can be linked through unique biometric data. Additionally, identity theft is a severe threat to identity management, if revocation and reissuing of biometric references are practically impossible.
Therefore, template protection techniques are developed to overcome these drawbacks and limitations of biometrics. Their advantage is the creation of multiple secure references from biometric data.
These secure references are supposed to be unlinkable and non-invertible in order to achieve the desired level of security and to fulfill privacy requirements.
The existing algorithms can be categorized into transformation-based approaches and biometric cryptosystems. The transformation-based approaches deploy different transformation or randomization functions, while the biometric cryptosystems construct secrets from biometric data.
The integration in biometric systems is commonly accepted in research and their feasibility according to the recognition performance is proved. Despite of the success of biometric template protection techniques, their security and privacy properties are investigated only limitedly.
This predominant deficiency is addressed in this thesis and a systematic evaluation framework for biometric template protection techniques is proposed and demonstrated: Firstly, three main protection goals are identified based on the review of the requirements on template protection techniques.
The identified goals can be summarized as security, privacy protection ability and unlinkability. Furthermore, the definitions of privacy and security are given, which allow to quantify the computational complexity estimating a pre-image of a secure template and to measure the hardness of retrieving biometric data respectively.
Secondly, three threat models are identified as important prerequisites for the assessment.
Threat models define the information about biometric data, system parameters and functions that can be accessed during the evaluation or an attack. The first threat model, so called naive model, assumes that an adversary has very limited information about a system.
In the second threat model, the advanced model, we apply Kerckhoffs' principle and assume that essential details of algorithms as well as properties of biometric data are known. The last threat model assumes that an adversary owns large amount of biometric data and this allows him to exploit inaccuracy of biometric systems.
It is called the collision threat model. Finally, a systematic framework for privacy and security assessment is proposed.
Before an evaluation process, protection goals and threat models need to be clarified. Based on these, the metrics measuring different protection goals as well as an evaluation process determining the metrics will be developed.
Both theoretical evaluation with metrics such as entropy, mutual information and practical evaluation based on individual attacks can be used. The framework for privacy and security assessment is applied on the biometric cryptosystems: I develop my own 3D face recognition algorithm based on the depth distribution of facial sub-surfaces and integrate it in the fuzzy commitment scheme.
The iris recognition is based on an open source algorithm using Gabor filter. It is implemented in the fuzzy commitment scheme with the two layer coding method as proposed by Hao et al. Both features, the 3D face features and the iris features, represent local characteristics of the modalities.
Thus, strong dependency within these features is observed. The second order dependency tree is applied to describe the distribution of 3D face features. The Markov model is applied to characterize the statistical properties of iris features.
Thus, security and privacy of these algorithms can be measured with theoretical metrics.We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy.
A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive.
biometrics template protection, and then present a novel chaos-based biometrics template protection with secure authentication scheme. The proposed scheme is lightened by fuzzy. erations on templates. If a template representation satisﬁes these constraints, the template is referred to as a “protected biometric template”. 2. Revocable, renewable, and diversiﬁable protected templates. Protected biomet-ric templates should support mechanisms for revocation (for example using cer-tiﬁcates from a certiﬁcate authority). And, one of the crucial steps in the design of a secure biometric system is protection of the users’ templates that are stored in a central database. Biometric template security is a very important issue due to the fact that unlike passwords and tokens, a compromised template cannot be revoked and reissued.
Related work Chapter 2 28 Secure and Revocable Biometric Template Using Fuzzy Vault for Fingerprint, Iris and Retina CHAPTER 2 2. RELATED WORK ATTACKS AGAINST BIOMETRIC SYSTEMS BIOMETRIC TEMPLATE PROTECTION METHODS HYBRID APPROACH OTHER TEMPLATE PROTECTION SCHEMES CHAPTER SUMMARY. Cloud security at AWS is the highest priority.
As an AWS customer, you will benefit from a data center and network architecture built to meet the requirements of the most security-sensitive organizations. A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger. The recovery of fingerprints from a crime scene is an important method of forensic regardbouddhiste.comprints are easily deposited on suitable surfaces (such as glass or metal or polished stone) by the natural secretions of sweat from the eccrine glands that are .
Template Protection for Fingerprint Recognition System using Fuzzy Vault Shubhangi Sapkal, Government College of Engineering, Aurangabad Biometric template protection scheme is used to convert biometric data into a secure form. It is impossible or hard to retrieve templates from secured biometric data, which.
The system enables businesses to Notify the Commissioner, as required by the Data Protection Law, by completing pre-set templates on-line, relevant to .