Amine Haytom
email: haytom.amine@testwe.eu

Christophe Rosenberger
email: christophe.rosenberger@ensicaen.fr

Christophe Charrier
email: christophe.charrier@unicaen.fr

Charles Zhu
email: charles.zhu@testwe.eu

Clément Régnier
email: clement.regnier@testwe.eu

Clément Régnier Bio- Metric

Biometric Application for authentication and management of online exams

Keywords: Personal data, Biometric signature, Learner behaviour. Biometric Application

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Today, with the rapid growth of online courses and exams, more and more students or professionals are applying for distance learning. Certainly, distance learning offers several advantages; the opportunity to take training in a distant establishment, arrange teaching for the personal and professional life of the learner, have great organizational flexibility and reduce accommodation and transportation costs.

It allows to have the same level obtained in classroom training. Secure tools must be used to safely access the exams. It is essential to control a student's access and environment during an exam. Online assessment and supervised tests address the issue of student verification and the environment in which access to unauthorized documents and resources is a major problem Data processing and environmental control must respect the principles encouraged by the European regulation on the protection of personal information. The information often collected in a regulatory context can make the verification of a person a safe and secure step. Higher education that encourages schools to fight cheating more effectively, it is more interested in the quality and integrity of online proctoring. This work can be divided into two parts; users authentication solution based on facial recognition and the integration of a security system to detect fraud during online exams.

A biometric model is proposed to maintain the integrity and quality of the online assessment; at beginning, the system integrates face recognition modality. Biometrics refers precisely to a computer technology that stimulates the physical presence of an individual, it conceives its identity and its movements as sources of dangers and risks, it represents a new era of modernity.

Generally, biometrics is divided into two categories; physical and behavioural. Physical analysis include the geometry of the hand, the retina, and the characteristics of the iris or the facial features. Behavioural analysis helps to verify a person's identity by examining measurable activity, such as gesture recognition and keystroke dynamics.

The integration of other modalities such as speaker recognition or detection allows to improve the efficiency of exam management system. This paper represents information about using face recognition to obtain biometric authentication of a user.

Facial recognition

Many applications nowadays require personal data about the user, where the proof of identity is used to provide a secure service and to guarantee that a person is authorized to access an online multimedia service. In addition, it is necessary to convince users that the proposed solution is credible and that the information is processed reliably.

Without a doubt, facial recognition could play a necessary role to improve the identity verification of individuals. This biometric solution has a very high degree of acceptability when compared to other modalities such as iris or gesture recognition.

Generalities

We present in this chapter a solution to have a biometric signature based on the physical characteristics of the learner that will be used as proof of identity. Actually, facial recognition will be integrated into the remote examination management system as a basic modality to increase the security level of the application.

The biometric signatures are part of a lot of so-called sensitive data. So, we implement security measures and data protection processes by obtaining an encrypted template in the form of a secure code The personal data are either material what the user possesses (QR-code, smart card, RFID tag), behavioural what the user knows how to do (typing dynamics and gesture recognition) or physiological data that which is related to the user (face, fingerprint, iris, voice).

1.1.2

Face recognition approaches Nowadays, facial recognition systems have become an indispensable tool in many fields of application. It remains an acceptable solution for many people because it is less expensive and easier to use.

Facial recognition systems are well-known computer vision applications whose purpose is to verify or identify a person. There are two main approaches to facial recognition; Facial recognition systems are well-known computer vision applications whose purpose is to verify or identify a person on the measurement of selected facial features. There are different approaches for facial recognition; Global approach: technically speaking, this approach is based on the global analysis of the face. The advantages of this approach the low time of calculates, the limitations are related to the size of the sample used as well as the difficulty to compute higher order statistics. Local approach: this approach is based on the analysis of face via different techniques, such as; Geometric measurements, Elastic Graph Matching and so on.

Facial recognition systems exploit digital images to provide contactless authentication. These systems have become widespread due to increasing demand in the areas of security and automatic identity verification. Nevertheless, it is possible to find approach with good performance that work despite the different types of constraints, so that the person to identify is in a controlled environment, using cameras with a specific orientation.

However, testing in a non-controlled environment (variable lighting, variable orientation of the person towards the camera, moving person, etc.) remain problematic, because the verification of the users is carried out with high uncertainty especially during the acquisition phase.

Certainly, there are other methods to verify the identity of a person such as Deep Learning based approach. To address the critical environmental issue, the research activity focused on the analysis of human perception in order to describe mathematically the processes that lead to the recognition of actions. Analysing human brain way to recognize faces and objects. Implementing algorithms operating in uncontrolled environments with a high level of accuracy. Today, there are several works and scientific publications based on artificial intelligence for user verification and identification.

Face recognition is a constantly evolving field, continually improving. Identity verification through deep learning has recently received a lot of attention from the research and industry community and is starting to be applied in a variety of areas, mainly for security.

We can find study that handle the authentication based on Convolution Neural Network [START_REF] Omkar | BMVC, cis.csuohio.edu[END_REF], researchers compute VGG-Face CNN descriptors using CNN implementation based on the VGG-Very-Deep-16 CNN architecture and they evaluate it on a large databases (Labeled Faces in the Wild and the YouTube Faces). In this manuscript, we will present the techniques used for the detection of faces and the authentication. Next, we examine the learners' activities to detect frauds. While respecting the privacy of individuals.

Related work

In the literature, we can find studies that deal with remote exam management. (Pierre Beust, Valérie Cauchard, Isabelle Duchatelle) (First results of the experiment of remote monitoring tests) conducted a real-life remote monitoring experiment at the University of Caen Normandie.

This experiment allowed students in different countries to take this exam. The experiment was carried out as part of the training at (C.2.i.) Level 1 at the University of Caen Normandy. It is about a national certification to obtain the Computer and Internet Certificate.

The first results obtained during the experiment; 80 students volunteered on the University's platform, with more than 50% of candidates going so far as to set up an account with an organization that handles exam management. 31 participants completed the test as part of this experiment and most were under 25 years old, seven were absent and eight candidates could not pass the test due to technical problems.

Methodology

On the first hand, to authenticate users we start by capturing images using laptop camera, pre-processing tool have been used to enhance the input data. We use a Haar filter solution for face detection and then crop and normalize the faces. Next, we extract features using the convolutional neural network model to obtain a user model representing the signature of the user.

With the biometric model, learners can access based on the threshold used for the verification process. First, we compare the reference model with the signature acquired during authentication using a score distance. The idea is that if the score obtained is higher than the threshold the user is allowed to take the exam. However, if the score is below the threshold, the access will be refused.

Test Protocol (Preliminary test)

First of all, to obtain biometric data, the information is collected from the individual via an integrated camera of a "ThinkPad S440" computer. Next, pre-processing is applied to the model to improve the quality of data to be analysed. Intel Core i7-4510U computer with a CPU of 2.00 and 2.60 [GHz] was used during the examination. The detection process is activated throughout the test by a user interface application developed by QT and C ++. The examination was performed under real conditions, the overall duration is 1 hour for each test, and we acquire an image every 3 seconds.

The first test consists in comparing a reference image with images acquired from a person authorized to take the examination (Figure .3), on the other hand the second test is to compare the reference image with a person is not authorized to pass examination (Figure .4). In the second step called authentication, the system must decide if the user provide the expected pattern: the features extracted from the present pattern are compared to the stored features.

Recognition process

More specifically, the performance evaluation is based on the match score between the registered user and the test user, the learner can have access according to a decision threshold. Thus, in the verification system, two types of errors are defined. When a valid identity claim is rejected, a false miss error occurs. Conversely, when an imposter identity claim is accepted, a false acceptance error appears.

Data analysis

On the first hand, Euclidean distance was used to compute the similarity score between two images. The number of acquisition during the test is 720 images. The value zero means that the system does not detect any face, so if the distance is between 0.1 and 0.7 it is a person authorized to pass the test. If the distance is greater than 0.7 it means that the person is not allowed to take the exam.

We see that the value of the threshold is 0.7, when the amplitude of our signal is higher than the threshold at a given moment we consider that the user cheated.

To improve the threshold some machine learning tools have been used in order to create fraud detection model by setting the threshold value automatically.

Results

The idea here is to use and compare some learning method to detect the fraud automatically creating fraud detection model to manage online proctoring. The dataset for training represented mostly by normal cases. We begin the process by classifying input data by removing all zeros, then applying resampling to obtain user pattern.

In fact, 15 samples have been used to train the model, each sample is represented as one instance which is time series of continuous distance between reference pattern and new patterns acquired during the examination experiment. The idea here is to highlight several machine learning tools to create model that will be used to detect fraud during an online exam. Here, some statistical tools such as "ROC curve" and "Area under curve" have been used to evaluate our biometric system. We can consider the system is ideal when the ratio value is equal to 1, more the value is lower more the performance decrease. To detect fraud, 15 samples were used to form our model. The target is a "normal case" or "fraud attempt". The first 10 vectors contain normal case values and the last 5 values of cheat attempts. After carrying out the experiment, as we can see SVM learning method represent the higher performance compared to the other methods Many machine learning methods have been used to form our model. Logistic regression, kNN, decision of tree and SVM. It is obvious that the One-class SVM method is the best solution for classifying and detecting fraud in a remote exam. To conclude, we can integrate One-Class SVM model into our biometric system to detect an anomaly or create a fraud detection model to improve online assessment.

Future work

A biometric model is proposed to maintain the integrity and quality of the online assessment; at beginning, the system integrates face recognition modality. On the second hand, we integrate others modalities such as keystroke dynamics, speech detection and recognition, gaze tracking to improve system security and minimize cheating attempts. Also we will try to include other solutions to detect spoofing attack

Conclusion

The subject of this project is in the field of biometrics and the protection of the privacy of individuals and is particularly interested in the creation of an application with several features: authentication, monitoring of online learners, fraud detection and protection of personal data.

This work addresses the general problem of the protection of the privacy of a user during a remote examination. It seeks to define the different forms it can take fraud, as well as the expected properties of a secure biometric anti-cheat system that respects the privacy of learners.

Biometric technology is mainly used for security and smart applications. We should be careful about the drawbacks of ethical and social question raised by the use of biometrics.

For one, we can imagine a situation where a person's biometric information would be stolen, this could have hazardous consequences with respect to the right to privacy.

Regarding privacy, it remains a major concern because we are collecting data not only about a person, but very important information that makes this person unique having unique behavioural or physical signature.

To conclude, the integration of biometrics into distance learning systems will help teachers to effectively control student authentication, course tracking, provide certificates in an automated manner, analyse student behaviour during exams, the validation of the certificates of success as well as the detection of fraud.

  Many open source library to develop ML models like Keras, Tensor Flow and Caffe. As well as several databases, for example CelebFaces, Labeled Faces in the Wild and Public Figures Face. Deep learning remains an effective technique and works much better than many facial recognition methods.Today, surveillance systems are widely used for security purposes to monitor people in public places. A fully automated system is able to analyse information contained in the image or video through several image processing techniques Face Detection is a technique for searching the entire face in an image or video. This phase represents a major step for most face recognition systems. The recognition stage serves to verify the identity of a person from an image or video by comparing the reference images with those acquired during the enrollment phase.

Figure. 1 :

 1 Figure.1: Feature extraction process for pattern recognition

Face

  recognition systems involve two important phases. The first step concerns the user enrollment: Enrollment means the acquisition of the biometric input data, the detection of region of interest and the extraction of features to define a template of each genuine use.

Figure. 1 :

 1 Figure.1: Pre-processing steps

Figure. 2 :

 2 Figure.2: Steps for recognition To perform a recognition test, we begin by applying the important VGG pre-training model to our reference image (Figure.2). Then, a feature vector of the input image is obtained, which represents the reference model or the unique signature of the learner.

Figure. 3

 3 Figure.3 Curve represent user authorized to pass the exam As we can see, each point represents the resemblance score after the comparison of two images, the first image being the reference image captured during the enrollment phase. This image is compared with images acquired during the exam session

Figure. 2 :

 2 Figure.2: ROC curve to evaluate the performance of some learning classification tools.

  Table.1: Table to evaluate the performance of some learning algorithm (cross-validation value = 3).

	Algorithm	AUC	CA	F1	Precision	Recall
	Logistic Regression	0.889	0.5	0.444	0.792	0.5
	Tree	0.944	0.929	0.929	0.940	0.929
	SVM	1	1	1	1	1
	kNN	1	0.714	0.645	0.802	0.714


Takoua Guiga

Christophe Rosenberger

Jean-Jacques Schwartzmann

Behavioural Authentication Based on Smartphone Protected Personal Communication Data

Keywords: Behavioural Authentication, Smartphone, Privacy protection, BioHashing, Classification methods

Smartphones have become ubiquitous in everyday life, storing and generating a huge amount of sensitive personal data which make them vulnerable to increasing security and privacy threats. While protecting smartphones has become a necessity, existing traditional authentication methods, which are mainly PINs and passwords, are facing remarkable drawbacks and behavioural biometrics-based authentication was adopted as the best alternative to ensure better protection. This paper presents a comparative study of many behavioural authentication solutions using smartphone personal communication data. Different approaches are compared such as using Distance Minimization, K-means and Support Vector Machine (SVM) as classification method. The data privacy protection by using the BioHashing algorithm is also considered in the paper. The authentication approaches were tested on a dataset of 93 users with more than 16.000 samples and show promising results with an EER of 10% without any data protection with the One Class SVM method and an EER remarkably lower than 1% for the 3 adopted methods with data privacy protection.

I. INTRODUCTION

For the time being, what is not irritating and won't let you down? That true friend: your smartphone, which has become an essential companion in our daily life, on which we rely to do practically everything. The one that knows you more than anyone else by holding a huge amount of your personal information such as account credential and credit card details, business information, social media accounts, emails, images, voices,etc. Yet, that will remains true until the battery runs out, being lost or stolen, infected by malware or attacked by social engineering.

Hereby, the boundless use of smartphones for extended range of activities, and the increasing amount of sensitive personal data that are stored and generated, gives rise to security and privacy threats. Actually, any unauthorized access to this device, could have serious consequences and may turn it form a friend to a nightmare. According to Malware Statistics, trends and facts in 2019, presented by the security detective website [START_REF] Zacks | Malware statistics, trends and facts in 2019[END_REF], malware attacks have been increased for the last ten years across all mobile platforms, from 12.40 malware in 2009 to 812.67 malware in 2018, additionally to the augmented number of stolen or lost smartphones over the last few years. Thus, protecting this device is becoming a necessity.

Existing traditional authentication methods, which are mainly PINs codes and passwords, are facing remarkable drawbacks, while remaining vulnerable to different types of attacks as mentioned in the recent literature [START_REF] Berkman | The unbearable lightness of pin cracking[END_REF]. So, they are neither notably appropriate to use, as they are regularly forgotten, nor perfectly secure, since they are vulnerable enough to be guessed or stolen. This fact induces a usability problem as well, since 70% of smartphone users consider PINs and passwords really annoying and prefer not using them, reported by a recent study [START_REF] Wood | The use of passwords for controlled access to computer resources[END_REF].

Hence, in order to strengthen the security of these devices, researchers have expanded their interest in developing more performing authentication mechanisms based on biometric modalities, considering them unique to a single person and nearly impossible to compromise. It is about exploiting different data based on measurements and characteristics of user's body parts, called physiological modalities such as, face recognition, fingerprint, hand geometry, iris recognition, etc. However, none method is without limitation, it has been proved that these modalities have several drawbacks. Basically, physiological biometric data can be spoofed [START_REF] Akhtar | Biometric liveness detection: Challenges and research opportunities[END_REF] and are susceptible to potential privacy pitfalls. As discussed in [START_REF] Belguechi | An overview on privacy preserving biometrics[END_REF], it is true that biometric data are unique identifiers but they are not secret: fingerprint is leaved on everything we touch, faces can be easily acquired and voice can be simply recorded. Thus, the potential collection and use of biometric data without the knowledge of its owner, without his/her consent or personal control makes this information very sensitive. Consequently, always in order to improve authentication methods, behavioural biometric modalities have been revealed as the analysis of a behavioural trait, learned and acquired over time, considering the manner in which people react and how they perform something such as walking (Gait Recognition) [START_REF] Lee | Gait analysis for recognition and classification[END_REF], signing (Signature Recognition) [START_REF] Plamondon | Online and off-line handwriting recognition: a comprehensive survey[END_REF], and typing on the keyboard (Keystroke dynamics) [START_REF] Clarke | Authenticating mobile phone users using keystroke analysis[END_REF].

One of the main advantages of behavioral biometrics is being dependent on the user actions and habits, which makes them perfect candidates for transparent user authentication [START_REF] Alotaibi | Transparent authentication systems for mobile device security: A review[END_REF], as samples are recorded seamlessly. According to Nathan Clarke [START_REF] Clarke | Transparent user authentication: biometrics, RFID and behavioural profiling[END_REF], transparent authentication can be achieved by any authentication approach that is able to obtain the sample required for verification non-intrusively. This paper presents, in one hand, a comparative study of behavioural authentication using smartphone communication data by using different methods such as distance Minimization, K-means and Support Vector Machine (SVM) as classification techniques. On the other hand, this paper analyses the efficiency of data privacy protection by adopting the biohashing algorithm. In this study, we use real data from 93 users with more than 16.000 samples.

Section II describes the related works on behavioral authentication solutions on mobile devices. Section III presents the used classification methods (distance minimization, Kmeans, SVM) and the BioHashing algorithm for the privacy data protection. Section IV describes the proposed protocol and details the used dataset. Section V reports obtained results and section VI concludes the paper and present some perspectives and future works.

II. RELATED WORKS

This section is dedicated to a state of the art both for behavioral authentication on mobile devices and privacy issues of biometrics.

Behavioral authentication solutions that provide transparent authentication are a fast growing area. This is especially due to the Active Authentication project [START_REF] Guidorizzi | Security: Active authentication[END_REF]. The Defense Advanced Research Projects Agency offers to move beyond password by using transparent authentication mechanism. This means most users will authenticate themselves using biometric sensors. Thus, Google announced in May 2016 the Abacus project [START_REF] Google | Google Abacus project[END_REF], a multi-modal, seamless and continuous authentication system designed to replace the login/password pair. The authors of [START_REF] Hayashi | Casa: Contextaware scalable authentication[END_REF] proved that combining the location with a standard authentication increases the global trust in that authentication. In addition, this article shows that the two main locations arising for a user are home and workplace. This implies to continuously know where the user is and therefore compromises users privacy. The location property and especially the one offered by the Global Positioning System (GPS) sensors embedded in modern smartphones represents relevant features.

The authors in [START_REF] Li | Active authentication for mobile devices utilising behaviour profiling[END_REF] offer a solution to authenticate users using the geolocation and the phone calls. They obtain an EER of 5.4% with the 6 last phone calls. However, the privacy aspect is not taken into account. In [START_REF] Saevanee | Text-based active authentication for mobile devices[END_REF], the authors combine different authentication modalities and also include the text message content. To proceed with the text message information, the messages must be read. This implies a privacy leakage.

Less sensitive data can be exploited to perform behavioral authentication. This is the case of gait recognition [START_REF] Derawi | Gait and activity recognition using commercial phones[END_REF]. However, the authors in [START_REF] Tanviruzzaman | Your phone knows you: Almost transparent authentication for smartphones[END_REF] have shown that combining location information with gait recognition increases the global performance of the system. By combining those data, they obtained an ERR of 10% on a dataset of 13 users. However, privacy protection is not taken into account.

To the best of the authors knowledge, there are few proposed solutions in the literature dealing with privacy concerns. The authors in [START_REF] Safa | Privacy-preserving implicit authentication[END_REF] use an homomorphic encryption scheme. In [START_REF] Nauman | Using trusted computing for privacy preserving keystroke-based authentication in smartphones[END_REF], the authors address the problem of online authentication using implicit information and store the data directly on the mobile phone, thus delegating the authorization server role to the mobile phone. This permits to mitigate the privacy problem but does not solve the cancellability issue.

In [START_REF] Hatin | Privacy Preserving Transparent Mobile Authentication[END_REF], the authors considered privacy issues in the designing of an authentication scheme based biometric features. This solution permits to solve both the privacy problem and cancellability issue. This paper aims at improving this solution by using less sensitive information (phone calls statistics). In the next section, different classification methods are compared to reach this objective.

III. PROPOSED METHODS

This section first presents different classification techniques used in the authentication system in order to evaluate their relative performance. We start with a simple distance based minimization method (Distance minimization), then applying an unsupervised classification algorithm (K-means), to finish with a supervised classification algorithm (SVM). Second, we focus on privacy protection.

A. Classification methods 1) Distance minimization: It represents a simple technique consisting in affecting the unknown individual to the class having the most similar individuals. Considering a dataset (X i ,U i ), i=1:n with U i having K different values (K classes). An unknown individual is affected to the class J if the distance between its parameters and an individual belong to the class J is minimal among all individuals.

2) K-Means: It is one of the most popular unsupervised algorithm. Generally, unsupervised algorithms make decisions from datasets using only input vectors without implying known or labelled classes. K-means stores k centroids that uses to define clusters. A data point is considered to be in a particular cluster if it is closer to that cluster's centroid than any other centroid. Based on [START_REF] Piech | Kmeans[END_REF], in the clustering problem,a training set x 1 ,...,x m is given, and the goal is to group the data into a few cohesive clusters. Principally, For given feature vectors for each data point x i ∈R n , the intention is to predict k centroids and a label C i for each data point. The k-means clustering algorithm is as follows:

• Initialize cluster centroids u 1 ,u 2 ,...,u k ∈ R n randomly • Repeat until convergence:
For every i, set

C i = arg min j (||x i -u j || 2 ) (1)
For each j, set

u j = m i=1 1{C i = j}x i m i=1 1{C i = j} (2)
3) SVM: The Support Vector Machine (SVM) classification algorithm, is among the best supervised learning algorithms and is widely used in many types of applications [START_REF] Ng | Cs229 lecture notes[END_REF]. This technique is a two-class classification method that aims generally to separate negative data from positive ones. The algorithm then searches for the hyperplane (in the linear case) that ensures this separation by maximizing the margin distance between the two classes. Given a set of learning data (x i , y i ) for i=1..n (n = size of data), with x i ∈ R d and y i ∈ {-1, 1}, train a classifier to find:

f(x i ) = < 0 if y i = -1 ≥ 0 if y i = 1 (3)
The decision function f(x i ) depends on whether the data are linearly separable or not. When data are linearly separable, the decision function is as follows:

f(x i ) = n i=1 w i x i + b (4)
where w i ∈R n is the weight vector, b the bias, and x i the data variable. In most classification problems, the data are non-linearly separable. So the solution to classify this data is to project them into a larger space where the data becomes linearly separable using a function called Kernel K, and the decision function becomes:

f(x i ) = n i=1 w i K(x i , x) + b (5)
The kernel depends on the number of data and the complexity of the presented problem and it can be Linear, Sigmoid, Polynomial or RBF as shown in the following table I:

B. Privacy protection

In this paper, the use of smartphone communication data for the user authentication is investigated. As the verification process could be done by a server considered as honest but curious, a privacy protection of collected data is required.

The concept of privacy protection of biometric data has been defined in 2001 in a seminal paper [START_REF] Ratha | Enhancing security and privacy in biometrics-based authentication systems[END_REF]. Since then, many methods have been proposed among random projections approaches [START_REF] Pillai | Sectored random projections for cancelable iris biometrics[END_REF], BioHashing methods [START_REF] Teoh | Biohashing: two factor authentication featuring fingerprint data and tokenised random number[END_REF], Bloom filters [START_REF] Rathgeb | On application of bloom filters to iris biometrics[END_REF], to cite just a few. A complete review of cancelable biometric systems can be found in [START_REF] Patel | Cancelable biometrics: A review[END_REF]. Very recently, Teoh et al. [START_REF] Jin | Ranking-based locality sensitive hashing-enabled cancelable biometrics: Index-of-max hashing[END_REF] proposed a new two-factor scheme to protect the biometric template by transformation. Compared with previous works, this method is based on localized random projection and on the rank correlation. Moreover, the obtained results show that this system is strongly resistant against the main attacks. These good results are the consequences of their technical called Index-Of-Max which can be viewed as a machine learning on the plain database. For this previous constraint, the comparaison to this method is not adopted where the BioSystem is tuned for a particular basis. More generally, a security analysis of the biometric system protecting the biometric template based on transformations [START_REF] Rosenberger | Evaluation of biometric template protection schemes based on a transformation[END_REF] are considered. In the following, the BioHashing algorithm is particularly detailed [START_REF] Teoh | Biohashing: two factor authentication featuring fingerprint data and tokenised random number[END_REF], one of the popular template protection schemes.

The BioHashing algorithm is applied on biometric templates that are represented by real-valued vectors of fixed length (so the metric used to evaluate the similarity between two biometric features is the Euclidean distance). It generates binary templates of length lower than or equal to the original length (here, the metric D T used to evaluate the similarity between two transformed templates is the Hamming distance). This algorithm has been originally proposed for face and fingerprints by Teoh et al. in [START_REF] Teoh | Biohashing: two factor authentication featuring fingerprint data and tokenised random number[END_REF]. Then, the BioHashing algorithm transforms the biometric template T = (T 1 , . . . T n ) into a binary template B = (B 1 , . . . B m ), with m ≤ n, as following:

The specificity of the BioHashing algorithm is that it uses a one way function and a random seed of m bits. It is important to note that every enrolled biometric feature uses a different seed in order to create a specific BioCode. The performance of this algorithm is ensured by the scalar products with the orthonormal vectors. The quantization process of the last step ensures the non-invertibility of the data (even if n = m, because each coordinate of the input T is a real value, whereas the coordinates of the output B is a single bit). Finally, the random seed guarantees both the diversity and revocability properties.

B i = 0 if x i < τ 1 if x i ≥ τ,
where τ is a given threshold, generally equal to 0.

IV. PROPOSED PROTOCOL A. The smartphone personal dataset

The idea is to use behavioral data from a smartphone communication to authenticate a user. Thus, a real private dataset is collected, including the communication behavior of 93 users recorded during one month. Each user has 8 collected information as follows: The number of samples is different from a user to another, since data is collected in a real and unique way for each user (different numbers of calls, different callee, etc.). The total number of samples is 16143. Samples are then divided equally and randomly swapped into two sets: the reference dataset, dedicated to train the model and the test dataset is used to provide an evaluation of the model.

B. Protocol description

In this section, we describe the experimental protocol used to evaluate the proposed solution both in terms of performance and in terms of privacy protection.

In order to evaluate the capacity of the authentication system for a user based on his/her behaviour dataset described in section IV-A, three classification techniques have been used: The distance minimization technique, the K-means algorithm and the support vector machine (SVM) algorithm, as mentioned in section III-A.

It is about calculating a similarity score between samples of the test dataset and the model created by the reference dataset. The lower the score is, the higher is the similarity between them. It is checked whether the score higher than a certain threshold to correctly authenticate a user. For each user, two different scores are computed: the legitimate scores, comparing test samples of one user to the model of the same user and the impostor scores, comparing samples of all users with the model of one user.

The performance of the authentication system is evaluated by three main measures which are the False Acceptance Rate (FAR) defined as the proportion of times a system grants access to an unauthorized person, the False Rejection Rate (FRR) which is the proportion of times a biometric system fails to grant access to an authorized person and the Equal Error Rate (EER) defining the common value where the FAR and the FRR values intersect. The lower the EER is, the better is the system performance.

Two scenarios are adopted:

• Scenario 1: evaluating the system performance without data protection • Scenario 2: evaluating the system performance with data protection First, the three classification methods are performed separately in order to compare their performances using the behavioural dataset in terms of EER value (which has to be minnimized). Then, the Biohashing algorithm is applied in the interest of protecting biometric data privacy and to analyze its impact on classification algorithms in terms of authentication performance. Furthermore, as the reference dataset and the test dataset are constructed with a random draw for a set ratio. It is highly recommended to average the resulting EER value for each random draw. The ratio is the percent of dividing the training data and testing data. So, for a fixed value of a ratio, the EER value is computed n times, and then the average of the n obtained values is considered as the final EER value assigned to a fixed ratio.

V. RESULTS

This section presents the experimental results obtained in order to evaluate the system performance for both proposed scenarios.

1) Scenario 1: evaluating the system performance without any data protection: This first scenario basically evaluates the performance of the different classification algorithms presented in section III without any data protection, to figure out the best approach for a better authentication.

Distance minimization is implemented with a simple computation of the euclidean distance between the test dataset and the model described in the proposed protocol. A one Class SVM is used with RBF kernel which gives better results than other kernel types. K-means algorithm is computed with K=3 centroids. Results are presented in terms of EER values for a ratio = 80% (thus, 20% of data are used for testing) and are summarized in table III. The ROC curves presenting FRR values against FAR values for the 3 evaluated methods are exposed in figure 2. It is clear that better results are obtained with the One class SVM classifier with an EER= 10.4%. Furthermore, the EER values depend on the used ratio value. For this reason, the following figures 3, 4, 5 show respectively the EER variation depending on the ratio variation for the distance minimization, K-means and the one class SVM methods, in order to determine the best ratio for every method. Clearly for the 3 methods, the higher is the ratio, the lower is the EER. The ROC curves presenting FRR values against FAR values for the 3 evaluated methods with the BioHashing algorithm are exposed in figure 7. It is clearly remarkable that the performance of the 3 classification methods with the BioHashing algorithm is improved compared to the unprotected case. One class SVM remains the best with an EER= 0.10 %, but we can also notice that the performance of the K-means algorithm with privacy protection has improved significantly and the EER goes from a value of 37% to 0.7% , which is not the best result but is still interesting.

To better compare the difference, figures 8, 9 and 10 show respectively the performance with and without privacy protection of Distance Minimization method, K-means and One class SVM method. Also, as studied in the first scenario, the EER values vary according to the ratio values. For that, figure 11, 12,13 display the variation of EER depending on the ratio values with data privacy protection respectively for Distance Minimization method, K-means and One class SVM method.

We can see clearly that the proposed solutions with protection with the BioHashing algorithm provide very good results.

VI. CONCLUSION AND FUTURE WORK

This study presented a behavioural biometric authentication approach based on smartphone personal communication data in order to improve smartphone security and privacy protection of sensitive data by providing a robust user authentication. For this purpose, three classification methods were used to evaluate the proposed approach performances in terms of deciding on the best classifier for a better authentication based on EER value. In a second scenario, the data privacy is considered and protected with the BioHashing algorithm to evaluate the system performances with data protection.

The obtained experimental results are promising with an EER value of 10% for One Class SVM without data protection which make it the best classifier for a better authentication comparing to the other adopting classifiers. K-means method shows an EER value of 37% which is considered high in comparison with the Distance Minimization and One Class SVM techniques. However, when implementing the BioHashing algorithm for data privacy protection, EER values for the three methods are lower than 1% and One Class SVM classifier remains the best with an EER value of 0.1%. Great improvements are observed when using the BioHashing algorithm with the smartphone personal communication data which proves an interesting approach for a robust user authentication.

Concerning future works, combining this approach with other biometrics modalities is highly conceivable in order to improve user authentication. Furthermore, in a ubiquitous digital environment, multidevice authentication is becoming the solution for more secure and protected user authentication, where devices of the same user can interact mutually in order to ensure a high confidence score, sufficient enough to be able to communicate between them the authentication result found on a single device. For this reason, results with smartphone communication personal dataset found in this paper are considered to be associated to other types of devices (laptop for example),with the aim of ensuring more robust authentication in a ubiquitous digital environment.

•

  The start date and time of a call; • The phone number of the caller; • The phone number of the callee; • The type of communication (Text message or phone call); • The number of consumed units in a communication (seconds for a phone call, or number of SMS); • The type of a call (outgoing call, incoming call); • The latitude of a cell; • The longitude of a cell; A set of the 8 values defines each user profile representing his/her behavior based on a phone communication as shown in figure 1

Fig. 1 .

 1 Fig. 1. User behaviour based on smartphone communication data

Fig. 2 .

 2 Fig. 2. ROC curves for Distance Minimization, K-means and One class SVM methods (ratio=0.8)

Fig. 3 .

 3 Fig. 3. EER variation for Distance minimization method

Fig. 4 .

 4 Fig. 4. EER variation for K-means method2) Scenario 2: evaluating the system performance with data protection: In this second scenario, the data privacy is considered. Collected data are protected with the Biohashing algorithm. The Biohashing algorithm is implemented with the

Fig. 5 .

 5 Fig. 5. EER variation for One class SVM method

Fig. 6 .

 6 Fig. 6. Barcodes of different users BioCodes

Fig. 7 .

 7 Fig. 7. ROC curves for Distance Minimization, Kmeans and one class SVM with Biohashing (ratio=0.8)

Fig. 8 .Fig. 9 .Fig. 11 .

 8911 Fig. 8. ROC curves for Distance Minimization with and without Biohashing (ratio=0.8)

  Inputs 2: T = (T 1 , . . . , T n ): biometric template, 3: K z : secret seed 4: Output B = (B 1 , . . . , B m ): BioCode 5: Generation with the seed K z of m pseudorandom vectors V 1 , . . . , V m of length n, 6: Orthogonalize vectors with the Gram-Schmidt algorithm, 7: for i = 1, . . . , m do compute x i =< T, V i >.

	Algorithm 1 BioHashing
	1: 8: end for
	9: Compute BioCode:

TABLE II COMPARING

 II EER VALUES OF DISTANCE MINIMIZATION, K-MEANS AND ONE CLASS SVM , WITHOUT DATA PROTECTION

	Classification methods	EER (%)
	Distance Minimization	13.9
	K-means	37
	One class SVM	10.4

TABLE III COMPARING

 III EER VALUES OF DISTANCE MINIMIZATION, K-MEANS AND ONE CLASS SVM , WITH DATA PRIVACY PROTECTION

	Classification methods	EER (%)
	Distance Minimization	0.21
	K-means	0.74
	One class SVM	0.19


Cardiovascular diseases (CVD) remain the leading cause of mortality and a major cause of morbidity in Europe. Every year there are more than 6 million new cases of CVD in the EU and more than 11 million in Europe as a whole. With almost 49 million people living with the disease in the EU, the cost to the EU economies is €210 billion a year. There is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which, in extreme cases, can lead to sudden death. The objective of the Smart Autonomous Multi Modal Sensors for Vital Signs Monitoring (SmartVista) project is to develop and demonstrate a next generation, cost-effective, smart multimodal sensing platform to reduce incidences of sudden death caused by CVD, and will contribute to the EU vision of an Internet of Things for healthcare. The key innovation in SmartVista is to integrate 1D/2D nanomaterials-based sensors to monitor the heart, thermoelectric energy harvesters to extract energy from the body to power the system and printable battery systems to store this energy. Together these will result in a self-powered device that will autonomously monitor the electrocardiograph, respiratory flow, oxygen flow and temperature of the patient. This information will then be transmitted wirelessly for online health processing. This real-time self-powered monitoring of a patient's health is currently not available. Thus, the technology that will be developed in SmartVista will position us at the forefront of digital health and wearable biosensor technology for wireless monitoring in hospitals and of remote patients, both of which are necessary in this era of an aging population. You can find more information about SmartVista project at the website www.smartvista.eu.

Illustration of SmartVista smart wearable device for continuous health monitoring.

The miniaturization led advances in microelectronics over 50 years have revolutionized our lives through fast computing and communication. Recent advances in the field are propelled by applications such as robotics, wearable systems, and healthcare etc. through More than Moore technologies. Often these applications require electronics to conform to 3D surfaces and this calls for new methods to realize devices and circuits on unconventional substrates such as plastics and paper. This lecture will present various approaches (over different time and dimension scales) for obtaining distributed electronics and sensing components on flexible and conformable substrates, especially in context with tactile or electronic skin (eSkin). These approaches range from distributed off-the-shelf electronics integrated on flexible printed circuit boards, to advanced alternatives such as eSkin by printed nanowires, graphene and ultra-thin chips, etc. The technology for such sensitive flexible (and possibly stretchable) electronic systems is also the key enabler for numerous emerging fields such as internet of things, smart cities and mobile health etc. This lecture will also discuss how the flexible electronics research may unfold in the future.

Surface enhanced infrared absorption spectroscopy for molecule detection Fernando Gonzalez-Posada Flores Institut d'Electronique et des Systemes, Montpellier

Making the right diagnosis in the case of illness is a crucial but not an easy task. Generally, it relies on invasive medical testing. Eventually, patient's medical status is related to pollutants in their environment. Optical lab-on-chip have recently emerged as a technique to identify moieties of molecule quantities notably in human laboratory analysis. Better and precise information from biological samples need information from a broadband spectral range. Molecules absorb light from the visible to far-IR spectral region. In this presentation, I will summarized the design, fabrication and characterization of sensitive semiconductor structures to address mid-IR range and cover the fingerprint region of molecules [1,2,3].

Among the family of 2D materials, transition metal dichalcogenides (TMDs) are intensively investigated for opto-electronic and electro-catalytic applications1,2. The properties of TMDs can be largely tuned by changing their elemental composition, their thickness and their atomic structure1. TMDs can adopt two different coordinations for the metal atoms: either a trigonal prismatic or an octahedral coordination corresponding to 2H and 1T phases respectively that can eventually induce dramatic changes in the electronic behavior of the nanosheets3. Besides phase engineering, the properties of 2D materials can be tuned by changing the defect concentration such as sulfur vacancies.

In addition we have recently demonstrated that TMD nanosheets can be covalently functionalized which opens new avenues for controlling the surface chemistry and the opto-electronic properties of exfoliated TMDs4. Through several examples, I will present strategies for engineering exfoliated 2D materials via phase, defect and functionalization. My presentation will highlight how these strategies can be used for fine tuning the properties of the 2D TMDs and other 2D materials for optoelectronics, electrocatalysis and molecular sieving.

15 October 2019 09:00 -09:10 Arrival & Welcome Note

	by Dr. Aida Todri-Sanial, CNRS-LIRMM
	Location: Bat 4, Salle Seminaire, LIRMM, 161 rue Ada, Montpellier
	09:10 -10:00 Soft, Conformable, Large Area eSkin
	by Prof. Ravinder Dahiya
	Bendable Electronics and Sensing Technologies
	School of Engineering, University of Glasgow, UK
	10:00 -10:50 Surface Enhanced Infrared Absorption Spectroscopy for
	Molecule Detection
	by Prof. Fernando Gonzalez-Posada Flores
	Institut d'Electronique et des Systemes (IES)
	University of Montpellier
	10:50 -

11:00 Coffee Break 11:00 -11:50 Engineering of Exfoliated 2D Materials: Principles and Applications

by Prof. Damien Voiry Institut Europeen des Membranes (IEM) University of Montpellier 11:50 -

12:40 Surface Nanocomposites for Sensing Applications: CNTs in PEEK by

	Prof. Wolfgang Basca
	CEMES-CNRS
	Université Paul Sabatier

12:40 -14:10 Lunch ABSTRACTS Soft, Conformable, Large Area eSkin

	Ravinder DAHIYA
	Bendable Electronics and Sensing Technologies (BEST) Group
	School of Engineering, University of Glasgow, G12 8QQ, UK

Surface nanocomposites for sensing applications : CNTs in PEEK Guillaume Pillet, Pascal Puech, Wolfgang Bacsa CEMES-CNRS and University of Toulouse

The formation of uniform dispersions from agglomerated carbon nanotubes in polymers is a major challenge for their use in composites. We show that agglomerated carbon nanotubes on top of PEEK, a high performance thermoplastic polymer, are efficiently incorporated into the surface when annealing. Annealing is found to disperse carbon nanotubes surprisingly well and a surface composite of about 200 nm thickness is formed. The tube dispersion is found to depend on tube type (multi, double or single walled). Electrical resistance measurements at perculation show strong sensitivity to deformation and are reversible for small deformations. We believe that controlled dispersion on the surface of a thermoplastic polymer is a promising system for sensing applications.


Dr Stefania Carapezzi

Dr Carapezzi

Approaches to TCAD Modeling for Nanoscaled Devices

Technology Computer Aided Design (TCAD) simulation tools are finite-element based programs which have promoted the development of bulk Silicon technology, by 1) exploring new device concepts, 2) providing physical insight of experimental data, and 3) allowing for sensitivity study of device characteristics. However, in the recent years the shrinkage of the sizes has been rapidly taking the MOSFET to its physical limit, where quantum effects have to be taken into account. To push MOSFETs to their limits and to explore devices that may complement or supersede them, calibrated and trustworthy TCAD simulations of these devices are indispensable in order to fully assess their benefits and limitations. In this talk, two case studies will be examined, dealing with different aspects of TCAD modeling of nanoscaled devices

10:30 -11:30 Nanomaterials for Sensing and Mechanical Energy Harvesting: Towards Autonomous Wearable Sensors Abstract: Wearable systems and flexible electronics have gained significant interest recently because of the wide range of applications they enable which includes health monitoring through tattoo-like sensory skin patches, foldable displays or communication devices, and tactile skin in robotics or prosthetics, etc. The reliable real-time operation of myriad sensors and electronics on such systems pretty much depend on the energy, which is currently supplied through conventional batteries having drawbacks such as bulky, non-flexible, limited lifespan, chemical hazard, and the periodic replacement etc. For wearable systems to be effective enabler of an application, especially where portability is necessary, energy autonomy is critical. The present talk delivers the concept of low-temperature processable organic / inorganic hybrid systems for the realization of inexpensive electronic devices including field-effect transistors (FETs), piezoelectric nanogenerators (PENGs), and resistive sensors on various polymeric substrates such as polyethylene terephthalate (PET), Polydimethylsiloxane (PDMS), etc. The developed devices will contribute in designing next-generation of wearable smart sensing systems to accurately collect data from the human body which relates to the individual's specific health conditions. Speaker: Dr. Abhishek S. Dahiya, IES, University of Montpellier, CNRS, Montpellier, France. Dr. Dahiya received his Master in Technology in nanoscience and nanotechnology from GGS Indraprastha University (India) in 2011 and the Ph.D. degree in Electronics (ZnO nanostructures for electronic and energy harvesting applications) from the GREMAN laboratory, Université François Rabelais de Tours, France in 2016. He performed his postdoctoral work at the GREMAN laboratory from 2016 to 2017 and the University of Bordeaux (ICMCB lab), between 2018 and 2019. Currently, he is working as a postdoctoral researcher at the IES-CNRS/Université de Montpellier in France. His research focus on the synthesis of metal oxide nanostructures and fabrication of nanodevices such as field-effect transistors, source gated transistors, mechanical and gas sensors, and piezoelectric energy harvesters.

11:30 -12:30 Carbon Nanotube Yarn Electrical Transport Study from Experiments to Semi-Empirical Modeling

Abstract: This work aims to develop new materials to replace metals in electrical wiring. Carbon nanotubes (CNT) are a good alternative as they show a high electrical conductivity as well as they can be assembled into yarns. However, CNT yarns have not yet reached the electrical conductivity of individual CNTs preventing them from competing with metals. All the published works on CNT yarns spun from CNT arrays reveal that their resistivities are limited above 1 mΩ.cm. In order to understand this apparent limitation, we present an extensive study of the CNT yarn electrical transport by measuring the yarn resistance from 3 K to 300 K. We show that the CNT yarn electrical transport is dominated by the CNT-CNT contact resistance below 70 K and by the intrinsic CNT resistance above. The CNT yarn electrical conductivity was improved by improving the CNT structural quality and by doping. The CNT structural quality improvement allows reaching a resistivity record of 0.76 mΩ.cm. From all our experimental studies and the literature data analysis, we developed a new electrical transport model that perfectly fits resistance from 3 K to 300 K for every CNT materials. At very low temperature, the transport follows the Luttinger Liquid theory whereas at higher temperature it depends on both the intrinsic CNT wall electrical transport (metallic or semi-conducting) and the CNT arrangement (bundled or individualized). Speaker: Dr. Yoann Dini, CEA-LITEN, Grenoble, France. Dr. Dini received his master of engineering degree in material science from the Ecole des Mines de Nancy (France) as well as a master of science degree in chemical engineering from the University of Alberta (Canada). In October 2019, he received his PhD degree in material science from the Université Grenoble Alpes. His PhD work was conducted at CEA, Grenoble and focused on understanding and improving the electrical transport in carbon nanotube yarns to reach high electrical conductivity applications. Dr. Dini has several articles published in the Carbon journal.