Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Boakes, Matthew; Guest, Richard; Deravi, Farzin; Corsetti, Barbara

doi:10.1109/tbiom.2019.2941728

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…In this case, biometric data would be continuously acquired in a passive way throughout normal device usage to constantly verify the user's traits. Diferent aspects such as modality, scenarios or environment, among others, can lead to alterations in the performance of mobile biometric systems [39]. Often combined, background sensors [10,176], touchscreen [150], and network information [108] are among the most frequent modalities explored to develop behavioural biometric continuous authentication systems.…”

Section: User Authenticationmentioning

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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Section: User Authenticationmentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

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“…Thus, the biometric data are continuously collected through a passive model during normal use of the mobile device, which ensures that the user’s physical features correspond to those of the legitimate owner. Nevertheless, several logical or environmental features, such as scenarios, modalities, or environmental traits, may adversely influence the accuracy of mobile biometric systems [ 99 ]. Thus, the literature reports hybrid solutions, which combine background sensors [ 100 , 101 ], touchscreen devices [ 102 ], and network information [ 103 ].…”

Section: Real-world Sensors Use Case Scenariosmentioning

confidence: 99%

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

Bocu

Явич

2022

Sensors

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The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.

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“…Compared to signature recognition, it introduces more challenges due to a lower amount of time samples captured and the lack of visual information feedback. It is well known that there are several aspects that influence authentication performance [5], including quality of sample and ability of the subject. A poor quality user template might cause a lack of robustness against attacks (i.e.…”

Section: Introductionmentioning

confidence: 99%