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

Hernández–Álvarez, Luis; Fuentes, José María de; González-Manzano, Lorena; Encinas, Luis Hernández

doi:10.3390/s21010092

Cited by 34 publications

(21 citation statements)

References 114 publications

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“…Proper authentication includes user authentication: whether the user has authorized access, and person identification: who the current user is ( Liang et al, 2020 ). Sensor-based user authentication uses biological features categorized in physical, physiological, and behavioral ( Hernández-Álvarez et al, 2021 ). Gait provides behavioral biometric-based authentication, which by comparison with knowledge-based (passwords, personal identification number (PIN)) and physiological biometric-based (face recognition, fingerprint) is unobtrusive, continuous, less prone to attacks, and easily tracked through wearable devices, videos, and smartphones in the context of IoT environments.…”

Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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“…Finally, it should be pointed out that the considered techniques should be complemented by widely deployed encryption protocols that would guarantee data security, such as hash functions, secret-key and public-key cryptography, among others [57,80].…”

Section: Other Perspectivesmentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

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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.

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“…Moreover, they examined 32 papers and assessed their citations and how training is performed. Recently, in 2021, the authors of [ 179 ] discussed privacy issues associated with sensor-based behavioral biometics. Moreover, they discussed a short overview of behavioral biometric-based approaches.…”

Section: Related Surveysmentioning

confidence: 99%

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

Baig

Eskeland

2021

Sensors

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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.

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

Cited by 34 publications

References 114 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

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

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