14 research outputs found
Edge-centric multimodal authentication system using encrypted biometric templates
Data security, complete system control, and missed storage and computing opportunities in personal portable devices are some of the major limitations of the centralized cloud environment. Among these limitations, security is a prime concern due to potential unauthorized access to private data. Biometrics, in particular, is considered sensitive data, and its usage is subject to the privacy protection law. To address this issue, a multimodal authentication system using encrypted biometrics for the edge-centric cloud environment is proposed in this study. Personal portable devices are utilized for encrypting biometrics in the proposed system, which optimizes the use of resources and tackles another limitation of the cloud environment. Biometrics is encrypted using a new method. In the proposed system, the edges transmit the encrypted speech and face for processing in the cloud. The cloud then decrypts the biometrics and performs authentication to confirm the identity of an individual. The model for speech authentication is based on two types of features, namely, Mel-frequency cepstral coefficients and perceptual linear prediction coefficients. The model for face authentication is implemented by determining the eigenfaces. The final decision about the identity of a user is based on majority voting. Experimental results show that the new encryption method can reliably hide the identity of an individual and accurately decrypt the biometrics, which is vital for errorless authentication
Biometric iris templates security based on secret image sharing and chaotic maps
Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
Developing an Algorithm for Securing the Biometric Data Template in the Database
This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric
template provides a dependable solution to the problem of user
verification in an identity control system. The template is saved
in the database during the enrollment and compared with query
information in the verification stage. Serious security and
privacy concerns can arise, if raw, unprotected data template is
saved in the database. An attacker can hack the template
information in the database to gain illicit access. A novel
approach of encryption-decryption algorithm utilizing a design
pattern of Model View Template (MVT) is developed to secure
the biometric data template. The model manages information
logically, the view shows the visualization of the data, and the
template addresses the data migration into pattern object. The
established algorithm is based on the cryptographic module of
the Fernet key instance. The Fernet keys are combined to
generate a multiFernet key to produce two encrypted files (byte
and text file). These files are incorporated with Twilio message
and securely preserved in the database. In the event where an
attacker tries to access the biometric data template in the
database, the system alerts the user and stops the attacker from
unauthorized access, and cross-verify the impersonator based on
the validation of the ownership. Thus, helps inform the users and
the authority of, how secure the individual biometric data
template is, and provided a high level of the security pertaining
the individual data privac
Fingerprint template protection schemes: A literature review
The fingerprint is the most widely used technology for identification or authentication systems, which can be known as fingerprint authentication systems (FAS).In addition to providing security, the fingerprint is also easy to use, very reliable and has a high accuracy for identity recognition. FAS is still exposed to security attacks because fingerprint information is unencrypted.Therefore, fingerprint information requires protection known as fingerprint template protection (FTP).This paper aims to provide an organized literature on FTP.Three research questions were formulated to guide the literature analysis.First, this
analysis focuses on the types of FTP schemes; second, the metrics used for evaluating the FTP schemes; and finally, the common datasets used for evaluating the FTP schemes. The latest information and references are analysed and classified based on FTP methods and publication year to obtain information related to the development and application of FTP.This study mainly surveyed 62 documents reported on FTP schemes between the year 2000 and 2017.The results of this survey can be a source of reference for other researchers in finding literature relevant to the FTP
Multiple classifiers in biometrics. part 1: Fundamentals and review
We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS
The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems.
Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance themThis work was funded by projects CogniMetrics (TEC2015-70627-R)
from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE
Multiple classifiers in biometrics. Part 2: Trends and challenges
The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key roleThis work was funded by projects CogniMetrics (TEC2015-70627-R)
from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of
this work was conducted during a research visit of J.F. to Prof. Ludmila
Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE
Key Protected Classification for Collaborative Learning
Large-scale datasets play a fundamental role in training deep learning
models. However, dataset collection is difficult in domains that involve
sensitive information. Collaborative learning techniques provide a
privacy-preserving solution, by enabling training over a number of private
datasets that are not shared by their owners. However, recently, it has been
shown that the existing collaborative learning frameworks are vulnerable to an
active adversary that runs a generative adversarial network (GAN) attack. In
this work, we propose a novel classification model that is resilient against
such attacks by design. More specifically, we introduce a key-based
classification model and a principled training scheme that protects class
scores by using class-specific private keys, which effectively hide the
information necessary for a GAN attack. We additionally show how to utilize
high dimensional keys to improve the robustness against attacks without
increasing the model complexity. Our detailed experiments demonstrate the
effectiveness of the proposed technique. Source code is available at
https://github.com/mbsariyildiz/key-protected-classification.Comment: Accepted to Pattern Recognitio
Differential Privacy Preservation in Robust Continual Learning
Enhancing the privacy of machine learning (ML) algorithms has become crucial with the
presence of different types of attacks on AI applications. Continual learning (CL) is a branch of ML with
the aim of learning a set of knowledge sequentially and continuously from a data stream. On the other hand,
differential privacy (DP) has been extensively used to enhance the privacy of deep learning (DL) models.
However, the task of adding DP to CL would be challenging, because on one hand the DP intrinsically
adds some noise that reduce the utility, on the other hand the endless learning procedure of CL is a serious
obstacle, resulting in the catastrophic forgetting (CF) of previous samples of ongoing stream. To be able to
add DP to CL, we have proposed a methodology by which we cannot only strike a tradeoff between privacy
and utility, but also mitigate the CF. The proposed solution presents a set of key features: (1) it guarantees
theoretical privacy bounds via enforcing the DP principle; (2) we further incorporate a robust procedure into
the proposed DP-CL scheme to hinder the CF; and (3) most importantly, it achieves practical continuous
training for a CL process without running out of the available privacy budget. Through extensive empirical
evaluation on benchmark datasets and analyses, we validate the ef cacy of the proposed solution.This work was supported by the Project Privacy Matters (PRIMA) under Grant H2020-MSCA-ITN-2019-860315. The work of
Julian Fierrez was supported by the Project Biometrics and Behavior for Unbiased and Trusted AI with Applications (BBforTAI)
under Grant PID2021-127641OB-I00 MICINN/FEDER