Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT
With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8689355/ |
| _version_ | 1819174793744744448 |
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| author | Nima Karimian Mark Tehranipoor Damon Woodard Domenic Forte |
| author_facet | Nima Karimian Mark Tehranipoor Damon Woodard Domenic Forte |
| author_sort | Nima Karimian |
| collection | DOAJ |
| description | With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules. |
| first_indexed | 2024-12-22T20:44:37Z |
| format | Article |
| id | doaj.art-af4f073abda0455d8aae3f517e2ec062 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:44:37Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-af4f073abda0455d8aae3f517e2ec0622022-12-21T18:13:16ZengIEEEIEEE Access2169-35362019-01-017491354914910.1109/ACCESS.2019.29107538689355Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoTNima Karimian0https://orcid.org/0000-0002-4590-7170Mark Tehranipoor1Damon Woodard2Domenic Forte3Department of Computer Engineering, San Jose State University, San Jose, CA, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USAWith the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.https://ieeexplore.ieee.org/document/8689355/Internet of ThingsECGbiometricquantizationPPGnoise |
| spellingShingle | Nima Karimian Mark Tehranipoor Damon Woodard Domenic Forte Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT IEEE Access Internet of Things ECG biometric quantization PPG noise |
| title | Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT |
| title_full | Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT |
| title_fullStr | Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT |
| title_full_unstemmed | Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT |
| title_short | Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT |
| title_sort | unlock your heart next generation biometric in resource constrained healthcare systems and iot |
| topic | Internet of Things ECG biometric quantization PPG noise |
| url | https://ieeexplore.ieee.org/document/8689355/ |
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