Biosec: a biometric authentication framework for secure and private communication among edge devices in iot and industry 4.0
Open accessAbstract
To address the privacy and security related challenges in Internet of Things (IoT) environment, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics with fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. In this way, BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Guaranteeing data security and confidentiality is one of the most difficult challenges when implementing AI systems in edge computing situations.
“While implementing AI systems in these kinds of situations, one of the most difficult challenges is going to be guaranteeing the data's security and confidentiality [114].”
From Edge AI: A survey (2023) · cites this paper as [114]
The Introduction (page unavailable) identifies data security and personal-data privacy as significant concerns in IoT, and the BioSec System section describes protecting biometric data on an edge device and in transit. The paper does not address implementing AI systems or rank this challenge among the most difficult, so the claim extends beyond its scope. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe major challenge to Edge Computing is security for users who may trust hardware manufacturers but lack access to identities of other components such as switches, routers, and base stations in publicly accessible modes.
“The major challenge to Edge Computing is that of security for users who may have confidence in hardware from manufacturers they trust but have no access to the identities of other components: switches, routers and base stations in publicly accessible modes [114,131].”
From Edge AI: A survey (2023) · cites this paper as [114]
The Introduction (page unavailable) identifies security as a significant issue in IoT environments, and the Motivation and Our Contributions section proposes secure communication among edge devices. Neither section supports the specific account of users trusting hardware manufacturers while lacking access to the identities of switches, routers, or base stations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTrust in the security of personal and socially sensitive data would increase if management of such data were ensured at the edge rather than in distant data centers.
“These authors also asserted that trust in the security of personal and socially sensitive data would be increased if the management of such sensitive data could be ensured at the edge rather than being centred in distant data centres [114].”
From Edge AI: A survey (2023) · cites this paper as [114]
The Introduction and BioSec System sections discuss protecting personal biometric data through authentication and encryption, including storage in a server database. Searches for trust and for edge-based management versus distant data centers found no passage asserting that managing sensitive data at the edge would increase trust. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
FVC2004: Third Fingerprint Verification Competition
Maio, D., Maltoni, D., Cappelli, R., et al. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25948-0_1 · Cited by 493 · see citing works
Aghili, S. F., Mala, H., Shojafar, M., & Lopez, P. P. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.020 · Cited by 187 · see citing works
RSS: An Energy-Efficient Approach for Securing IoT Service Protocols Against the DoS Attack
Ghahramani, M., Javidan, R., Shojafar, M., et al. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023102 · Cited by 53 · see citing works
A Privacy-Preserving Lightweight Biometric System for Internet of Things Security
Yang, W., Wang, S., Zheng, G., Yang, J., & Valli, C. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800378 · Cited by 47 · see citing works
SECURE: Self-Protection Approach in Cloud Resource Management
Gill, S. S., & Buyya, R. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.011791715 · Cited by 36 · see citing works
Self-citationMultimodal Biometric Authentication in IoT: Single Camera Case Study
Maček, N., Franc, I., Bogdanoski, M., et al. (2016) · Goce Delchev University Repository (Goce Delčev University of Štip) · Cited by 12 · see citing works
Security-Aware Autonomic Allocation of Cloud Resources
Gill, S. S., & Shaghaghi, A. (2020) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2020070102 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Self-citationFog computing: a taxonomy, systematic review, current trends and research challenges
Singh, J., Singh, P., & Gill, S. S. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.06.005 · Cited by 154 · see citing works
Self-citationGolec, M., Ozturac, R., Pooranian, Z., Gill, S. S., & Buyya, R. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3095466 · Cited by 77 · see citing works
Self-citation
- Authors
- Golec, M., Gill, S. S., Bahsoon, R., & Rana, O.
- Affiliations
- Queen Mary University of London (GB)
- University of Birmingham, U.K.
- Cardiff University (GB)
- Venue
- IEEE Consumer Electronics Magazine
- Published
- 2022-03-01
- Volume
- 11
- Issue
- 2
- Pages
- 51-56
- ISSN
- 2162-2248 (ISSN-L)
- 2162-2256
- OpenAlex
- W3100633408
- Cited by
- 58
- References
- 7
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 14 · 2023: 15 · 2022: 10 · 2021: 6
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- green
- qmro.qmul.ac.uk
- Licence
- Locations
- IEEE Consumer Electronics Magazine (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- Ministry of Education of the Turkish Republic
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- biometric authentication, fingerprint authentication, Internet of Things security, edge devices, Industry 4.0, biometric template protection, AES-128, privacy-preserving communication, consumer electronics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
