scholarly journals Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 92
Author(s):  
Luis Hernández-Álvarez ◽  
José María de Fuentes ◽  
Lorena González-Manzano ◽  
Luis Hernández Encinas
Keyword(s):  
State Of The Art ◽  
User Authentication ◽  
Privacy Preserving ◽  
User Profiling ◽  
Fundamental Aspect ◽  
Continuous Authentication ◽  
Private Data ◽  
Professional Information ◽  
Information Authentication ◽  
Biometric Features

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

scholarly journals Security, Privacy, and Usability in Continuous Authentication: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5967
Author(s):  
Ahmed Fraz Baig ◽  
Sigurd Eskeland
Keyword(s):  
User Authentication ◽  
Personal Data ◽  
Privacy Preserving ◽  
Sensor Data ◽  
Security Issues ◽  
Replay Attacks ◽  
Continuous Authentication ◽  
Security Challenges ◽  
User Friendly ◽  
Privacy Issues

Continuous authentication has been proposed as a possible approach for passive and seamless user authentication, using sensor data comprising biometric, behavioral, and context-oriented characteristics. Since these are personal data being transmitted and are outside the control of the user, this approach causes privacy issues. Continuous authentication has security challenges concerning poor matching rates and susceptibility of replay attacks. The security issues are mainly poor matching rates and the problems of replay attacks. In this survey, we present an overview of continuous authentication and comprehensively discusses its different modes, and issues that these modes have related to security, privacy, and usability. A comparison of privacy-preserving approaches dealing with the privacy issues is provided, and lastly recommendations for secure, privacy-preserving, and user-friendly continuous authentication.

scholarly journals Euler Movement Firefly Algorithm and Fuzzy Kernel Support Vector Machine Classifier for Keystroke Authentication

2019 ◽  
Vol 8 (11) ◽  
pp. 2267-2274
Keyword(s):  
Support Vector Machine ◽  
Firefly Algorithm ◽  
User Authentication ◽  
Modern Method ◽  
User Profiling ◽  
Support Vector ◽  
Free Text ◽  
Continuous Authentication ◽  
Kernel Support Vector Machine ◽  
Behavioural Biometrics

User authentication can be successfully employed using keyboard typing patterns which is a form of behavioural biometrics. This modern method is highly analyzed for static authentication which refers to typing of fixed texts like ‘password’ and ‘pin numbers’. Most of the methods with respect to keystroke dynamics are restricted to the study of user’s activity involving fixed text. The formulated work concentrates on the investigation of the log of the user activity focused on the keyboard usage within the computer system through free text which refers to typing of texts throughout the login session. The Buffalo dataset is used in User Profiling Similarity Measurement (UPSM) stage and to recognize the time slice of the users, Euler Movement Firefly Algorithm (EMFA) is utilized. The typing behaviour is formulated in the form of time series in User Profiling Continuous Keystroke Authentication (UPCKA). Moreover the progression is made to user’s Continuous Authentication so as to predict unauthorized users with the consideration of the classifier called Novel Fuzzy Kernel Support Vector Machine (NFKSVM). The experimental results provide the enhanced performance by utilizing the formulated UPCKA in correlation with the NFKSVM classifier when compared with SVM and Iterative Keystroke Continuous Authentication (IKCA) techniques

Privacy-Preserving Hybrid K-Means

2018 ◽  
Vol 14 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Zhiqiang Gao ◽  
Yixiao Sun ◽  
Xiaolong Cui ◽  
Yutao Wang ◽  
Yanyu Duan ◽  
...  
Keyword(s):  
Differential Privacy ◽  
State Of The Art ◽  
Privacy Preserving ◽  
Local Optimum ◽  
Massive Data ◽  
Data Sets ◽  
Second Stage ◽  
Private Data ◽  
Privacy Budget ◽  
Selection Of

This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

scholarly journals AuthCODE: A Privacy-preserving and Multi-device Continuous Authentication Architecture based on Machine and Deep Learning

2021 ◽  
pp. 102168
Author(s):  
Pedro Miguel Sánchez Sánchez ◽  
Lorenzo Fernández Maimó ◽  
Alberto Huertas Celdrán ◽  
Gregorio Martínez Pérez
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Continuous Authentication

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2011 ◽  
pp. 232-252
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

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