Piezoelectric Touch Sensing-Based Keystroke Dynamic Technique for Multi-User Authentication

Cui, Ziang; Huang, Anbiao; Chen, Junliang; Gao, Shuo

doi:10.1109/jsen.2021.3081518

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…SVM is a popular classification method, in part because it exhibits relatively high performance even with small training datasets. [91] In a groundbreaking study by Cui and co-workers, [92] a new method for secure multiuser authentication using piezoelectric touch panels was developed. This advanced technology captures unique keystroke dynamics by leveraging the duration and force of user touches.…”

Section: Svmmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

Zheng,

Chen,

et al. 2023

Advanced Intelligent Systems

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The rapid advancement of mobile devices and human–machine interaction technologies has ushered in a new era for flexible touch panels as a novel input interface. This article reviews the historical evolution and technical progress of flexible touch panel technologies, from rudimentary single‐point touch to sophisticated grid‐free touch systems. Additionally, the working principles and mechanisms that underpin these advanced systems, including capacitive, resistive, piezoelectric, and triboelectric nanogenerator technologies, are explored. Following this, the integration of machine learning methods into these panels is discussed, offering new avenues for enhancing user experience and expanding functional capabilities. Various machine learning algorithms such as support vector machines, artificial neural networks, convolutional neural networks, and k‐nearest neighbors are examined for their potential applications in touch panel technologies. Finally, the challenges and prospects for the application of flexible touch panels fused with machine learning are discussed.

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Section: Svmmentioning

confidence: 99%

“…In a groundbreaking study by Cui and co‐workers, [ 92 ] a new method for secure multiuser authentication using piezoelectric touch panels was developed. This advanced technology captures unique keystroke dynamics by leveraging the duration and force of user touches.…”

Section: Applications Of ML In Flexible Touch Panelsmentioning

confidence: 99%

Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

Zheng,

Chen,

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…However, multiple users should be treated as legal users in some applications. To address this issue, in [56], a piezoelectric-based technique for multi-user authentication is presented, in which the touch features are extracted from the information obtained from the piezoelectric touch panel, and the classification is done by a support vector machine (SVM). Finally, the classification accuracy reaches 97%, revealing the feasibility for multi-user authentication.…”

Section: Authenticationmentioning

confidence: 99%

“…The emerging applications based on the piezoelectric touch panels mentioned above are shown in Table 4. The EER of 0.720% [55] The accuracy of 97% Multi-user support [56] Mood detection…”

Section: Gait Analysismentioning

confidence: 99%

Piezoelectric Based Touch Sensing for Interactive Displays—A Short Review

Liu

2021

Materials

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Interactive display is an important part of electronic devices. It is widely used in smartphones, laptops, and industrial equipment. To achieve 3-dimensional detection, the piezoelectric touch panel gains great popularity for its advantages of high sensitivity, low cost, and simple structure. In order to help readers understand the basic principles and the current technical status, this article introduces the work principles of the piezoelectric touch panel, widely-used piezoelectric materials and their characteristics, as well as the applications of the piezoelectric touch panel. The challenges and future trends are also discussed.

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“…It usually uses learning methods to extract information representing user characteristics from different types of sensors on mobile devices to build a model. Such as face [6,7], fingerprint [8,9], voice [10,11], environmental location [12,13], keystroke behavior [14,15], finger movement [16,17], etc. However, the above-mentioned biometric information collection usually requires invoking the privacy-related permissions of the mobile device, which makes users worry that their privacy-related information may be leaked, and cannot take into account the security, privacy, and usability requirements of mobile authentication jointly at the same time.…”

Section: Introductionmentioning

confidence: 99%

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

Xiong,

Weng,

Liu

et al. 2024

CMES

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With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users' privacy information. At present, mobile user authentication methods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly includes: (1) Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication, reduce the probability of the model being bypassed by attackers, and reduce the overhead of data centralized processing and the risk of privacy leakage;(2) Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation; (3) Combine Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification. The experimental results on the real environment dataset of 1513 people show that the method proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

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Piezoelectric Touch Sensing-Based Keystroke Dynamic Technique for Multi-User Authentication

Cited by 11 publications

References 31 publications

Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

Piezoelectric Based Touch Sensing for Interactive Displays—A Short Review

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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