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A survey of privacy vulnerabilities of mobile device sensors

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arXiv 2021年

作者： Delgado-Santos, Paula Stragapede, Giuseppe Tolosana, Ruben Guest, Richard Deravi, Farzin Vera-Rodriguez, Ruben School of Engineering and Digital Arts University of Kent Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field. © 2021, CC BY-NC-ND.

关键词： Surveys

Mobile Behavioral biometrics for Passive Authentication

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arXiv 2022年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Acien, Alejandro Le Lan, Gaël Biometrics And Data Pattern Analytics Lab Universidad Autonoma De Madrid Spain Orange Labs Cesson-Sevigne France

Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval. © 2022, CC BY-NC-ND.

关键词： Authentication

Assessing the Quality of Swipe Interactions for Mobile Biometric Systems

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Assessing the Quality of Swipe Interactions for Mobile Biome...

IEEE International Joint Conference on biometrics (IJCB)

作者： Marco Santopietro Ruben Vera-Rodriguez Richard Guest Aythami Morales Alejandro Acien University of Kent Canterbury UK Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid Spain

The following topics are dealt with: face recognition; feature extraction; biometrics (access control); convolutional neural nets; learning (artificial intelligence); deep learning (artificial intelligence); iris reco... 详细信息

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

关键词： Feature extraction biometrics (access control) Face recognition Authentication Statistics Sociology Task analysis

Mobile Keystroke biometrics Using Transformers

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arXiv 2022年

作者： Stragapede, Giuseppe Delgado-Santos, Paula Tolosana, Ruben Vera-Rodriguez, Ruben Guest, Richard Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain School of Engineering University of Kent United Kingdom

Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowledge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature. © 2022, CC BY-NC-ND.

关键词： biometrics

Exploring Transformers for Behavioural biometrics: A Case Study in Gait Recognition

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arXiv 2022年

作者： Delgado-Santos, Paula Tolosana, Ruben Guest, Richard Deravi, Farzin Vera-Rodriguez, Ruben School of Engineering University of Kent United Kingdom Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures. © 2022, CC BY-NC-ND.

关键词： biometrics

BeCAPTCHA-Type: Biometric Keystroke data Generation for Improved Bot Detection

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BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

IEEE Computer Society Conference on Computer Vision and pattern Recognition Workshops (CVPRW)

作者： Daniel DeAlcala Aythami Morales Ruben Tolosana Alejandro Acien Julian Fierrez Santiago Hernandez Miguel A. Ferrer Moises Diaz Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain University Las Palmas Gran Canaria Spain

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology.

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GaitPrivacyON: Privacy-Preserving Mobile Gait biometrics using Unsupervised Learning

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arXiv 2021年

作者： Delgado-Santos, Paula Tolosana, Ruben Guest, Richard Vera-Rodriguez, Ruben Deravi, Farzin Morales, Aythami School of Engineering University of Kent United Kingdom Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) two convolutional Autoencoders with shared weights that transform attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation;and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoders) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. Two experimental studies have been examinated: i) MotionSense and MobiAct databases;and ii) OU-ISIR database. The experimental results achieved suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 96.6% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way. © 2021, CC BY-NC-ND.

关键词： biometrics

BeCAPTCHA-Type: Biometric Keystroke data Generation for Improved Bot Detection

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arXiv 2022年

作者： DeAlcala, Daniel Morales, Aythami Tolosana, Ruben Acien, Alejandro Fierrez, Julian Hernandez, Santiago Ferrer, Miguel A. Diaz, Moises Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain University Las Palmas Gran Canaria Spain

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on the bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, in few-shot learning scenarios it represents an important challenge. Furthermore, these results show the great potential of the presented models. © 2022, CC BY-NC-ND.

关键词： biometrics

Keystroke biometrics in response to fake news propagation in a global pandemic

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arXiv 2020年

作者： Morales, Aythami Acien, Alejandro Fierrez, Julian Monaco, John V. Tolosana, Ruben Vera-Rodriguez, Ruben Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Naval Postgraduate School MonterreyCA United States

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics