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Saideh M, Jamont JP, Vercouter L. Opportunistic Sensor-Based Authentication Factors in and for the Internet of Things. SENSORS (BASEL, SWITZERLAND) 2024; 24:4621. [PMID: 39066019 PMCID: PMC11281295 DOI: 10.3390/s24144621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/13/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.
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Affiliation(s)
- Marc Saideh
- INSA Rouen Normandie, Normandie Université, LITIS UR 4108, 76000 Rouen, France;
| | | | - Laurent Vercouter
- INSA Rouen Normandie, Normandie Université, LITIS UR 4108, 76000 Rouen, France;
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2
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Yang W, Wang S, Cui H, Tang Z, Li Y. A Review of Homomorphic Encryption for Privacy-Preserving Biometrics. SENSORS (BASEL, SWITZERLAND) 2023; 23:3566. [PMID: 37050626 PMCID: PMC10098691 DOI: 10.3390/s23073566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.
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Affiliation(s)
- Wencheng Yang
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Song Wang
- School of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Hui Cui
- Faculty of IT, Claytyon Campus, Monash University, Clayton, VIC 3800, Australia
| | - Zhaohui Tang
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
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3
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Sardar A, Umer S. Implementation of face recognition system using BioCryptosystem as template protection scheme. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2022. [DOI: 10.1016/j.jisa.2022.103317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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4
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Bedari A, Wang S, Yang W. A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7609. [PMID: 36236704 PMCID: PMC9572055 DOI: 10.3390/s22197609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.
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Affiliation(s)
- Aseel Bedari
- Department of Engineering, La Trobe University, Bundoora, VIC 3086, Australia
| | - Song Wang
- Department of Engineering, La Trobe University, Bundoora, VIC 3086, Australia
| | - Wencheng Yang
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
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5
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Mendi AF. A Digital Twin Case Study on Automotive Production Line. SENSORS (BASEL, SWITZERLAND) 2022; 22:6963. [PMID: 36146313 PMCID: PMC9506524 DOI: 10.3390/s22186963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
The manufacturing sector is one of the areas where the advantages of digital twin technology can benefit mostly. The product development, including its software, electronics, mechanics, and physical behavior, is included in the digital twin of the product. Furthermore, simultaneous data capturing from the sensors and data processing are also available in the digital twin. This enables each phase of the development cycle to be simulated, processed, and validated to discover the potential problems before the production of real components. In this study, the use of digital twin technology in the commercial production phase of the automotive production line with a case study is introduced. This study is one of the most comprehensive studies in the literature related to automotive production; therefore, it puts forth the power of using digital twin technology in that area. As the result of this case study, a 6.01% increase in the commercial production line efficiency and an 87.56% gain for downtime are achieved.
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Affiliation(s)
- Arif Furkan Mendi
- HAVELSAN, Information and Communication Technologies, 06510 Ankara, Turkey; or
- Department of Computer Engineering, Ostim Technical University, 06370 Ankara, Turkey
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Abstract
Much of precision medicine is driven by big health data research-the analysis of massive datasets representing the complex web of genetic, behavioral, environmental, and other factors that impact human well-being. There are some who point to the Common Rule, the regulation governing federally funded human subjects research, as a regulatory panacea for all types of big health data research. But how well does the Common Rule fit the regulatory needs of this type of research? This article suggests that harms that may arise from artificial intelligence and machine-learning technologies used in big health data research-and the increased likelihood that this research will affect public policy-mean it is time to consider whether the current human research regulations prohibit comprehensive, ethical review of big health data research that may result in group harm.
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Affiliation(s)
| | - Sara Meeder
- Director of Human Research Protections at Maimonides Medical Center
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Abstract
A promising approach to overcome the various shortcomings of password systems is the use of biometric authentication, in particular the use of electroencephalogram (EEG) data. In this paper, we propose a subject-independent learning method for EEG-based biometrics using Hilbert spectrograms of the data. The proposed neural network architecture treats the spectrogram as a collection of one-dimensional series and applies one-dimensional dilated convolutions over them, and a multi-similarity loss was used as the loss function for subject-independent learning. The architecture was tested on the publicly available PhysioNet EEG Motor Movement/Imagery Dataset (PEEGMIMDB) with a 14.63% Equal Error Rate (EER) achieved. The proposed approach’s main advantages are subject independence and suitability for interpretation via created spectrograms and the integrated gradients method.
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Abstract
Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.
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9
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Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches. ELECTRONICS 2022. [DOI: 10.3390/electronics11030383] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided.
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Sun Y, Leng L, Jin Z, Kim BG. Reinforced Palmprint Reconstruction Attacks in Biometric Systems. SENSORS 2022; 22:s22020591. [PMID: 35062552 PMCID: PMC8781289 DOI: 10.3390/s22020591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 02/05/2023]
Abstract
Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.
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Affiliation(s)
- Yue Sun
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, China; (Y.S.); (Z.J.)
| | - Lu Leng
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, China; (Y.S.); (Z.J.)
- Correspondence: (L.L.); (B.-G.K.)
| | - Zhe Jin
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, China; (Y.S.); (Z.J.)
- School of Artificial Intelligence, Anhui University, Hefei 230039, China
| | - Byung-Gyu Kim
- Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Korea
- Correspondence: (L.L.); (B.-G.K.)
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Jirayupat C, Nagashima K, Hosomi T, Takahashi T, Samransuksamer B, Hanai Y, Nakao A, Nakatani M, Liu J, Zhang G, Tanaka W, Kanai M, Yasui T, Baba Y, Yanagida T. Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning. Chem Commun (Camb) 2022; 58:6377-6380. [DOI: 10.1039/d1cc06384g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The potential feasibility of breath odor sensing-based individual authentication was demonstrated by a 16-channel chemiresistive sensor array and machine learning.
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Affiliation(s)
- Chaiyanut Jirayupat
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japan
| | - Kazuki Nagashima
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan
| | - Takuro Hosomi
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan
| | - Tsunaki Takahashi
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan
| | - Benjarong Samransuksamer
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yosuke Hanai
- Panasonic Corporation, Industry Company, Sensing Solutions Development Center, Kadoma 1006, Kadoma, Osaka 571-8506, Japan
| | - Atsuo Nakao
- Panasonic Corporation, Industry Company, Sensing Solutions Development Center, Kadoma 1006, Kadoma, Osaka 571-8506, Japan
| | - Masaya Nakatani
- Panasonic Corporation, Industry Company, Sensing Solutions Development Center, Kadoma 1006, Kadoma, Osaka 571-8506, Japan
| | - Jiangyang Liu
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Guozhu Zhang
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Wataru Tanaka
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Masaki Kanai
- Institute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan
| | - Takao Yasui
- PRESTO, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan
- Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Yoshinobu Baba
- Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Takeshi Yanagida
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japan
- Institute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan
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Rocha-Jácome C, Carvajal RG, Chavero FM, Guevara-Cabezas E, Hidalgo Fort E. Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2021; 22:66. [PMID: 35009609 PMCID: PMC8747394 DOI: 10.3390/s22010066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/18/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
Currently, the concept of Industry 4.0 is well known; however, it is extremely complex, as it is constantly evolving and innovating. It includes the participation of many disciplines and areas of knowledge as well as the integration of many technologies, both mature and emerging, but working in collaboration and relying on their study and implementation under the novel criteria of Cyber-Physical Systems. This study starts with an exhaustive search for updated scientific information of which a bibliometric analysis is carried out with results presented in different tables and graphs. Subsequently, based on the qualitative analysis of the references, we present two proposals for the schematic analysis of Industry 4.0 that will help academia and companies to support digital transformation studies. The results will allow us to perform a simple alternative analysis of Industry 4.0 to understand the functions and scope of the integrating technologies to achieve a better collaboration of each area of knowledge and each professional, considering the potential and limitations of each one, supporting the planning of an appropriate strategy, especially in the management of human resources, for the successful execution of the digital transformation of the industry.
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Electrical Impedance of Upper Limb Enables Robust Wearable Identity Recognition against Variation in Finger Placement and Environmental Factors. BIOSENSORS-BASEL 2021; 11:bios11100398. [PMID: 34677354 PMCID: PMC8534261 DOI: 10.3390/bios11100398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/06/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022]
Abstract
Most biometric authentication technologies commercialized in various fields mainly rely on acquired images of structural information, such as fingerprints, irises, and faces. However, bio-recognition techniques using these existing physical features are always at risk of template forgery threats, such as fake fingerprints. Due to the risk of theft and duplication, studies have recently been attempted using the internal structure and biological characteristics of the human body, including our previous works on the ratiometric biological impedance feature. However, one may still question its accuracy in real-life use due to the artifacts from sensing position variability and electrode-skin interfacing noise. Moreover, since the finger possesses more severe thermoregulatory vasomotion and large variability in the tissue properties than the core of the body, it is necessary to mitigate the harsh changes occurring at the peripheral extremities of the human body. To address these challenges, we propose a biometric authentication method through robust feature extraction from the upper-limb impedance acquired based on a portable wearable device. In this work, we show that the upper limb impedance features obtained from wearable devices are robust against undesirable factors such as finger placement deviations and day-to-day physiological changes, along with ratiometric impedance features. Overall, our upper-limb impedance-based analysis in a dataset of 1627 measurement from 33 subjects lowered the classification error rate from 22.38% to 4.3% (by a factor of 5), and further down to 2.4% (by a factor of 9) when combined with the ratiometric features.
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