Securing the Internet of Medical Things with ECG‐based PUF encryption
Abstract The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditi...
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Wiley
2024-06-01
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| Colecção: | IET Cyber-Physical Systems |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1049/cps2.12089 |
| _version_ | 1827227405047037952 |
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| author | Biagio Boi Christian Esposito |
| author_facet | Biagio Boi Christian Esposito |
| author_sort | Biagio Boi |
| collection | DOAJ |
| description | Abstract The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource‐constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre‐processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context. |
| first_indexed | 2025-03-21T17:57:26Z |
| format | Article |
| id | doaj.art-e6c98d0b7a1b40b18a20a4833d1f9bb8 |
| institution | Directory Open Access Journal |
| issn | 2398-3396 |
| language | English |
| last_indexed | 2025-03-21T17:57:26Z |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Cyber-Physical Systems |
| spelling | doaj.art-e6c98d0b7a1b40b18a20a4833d1f9bb82024-06-11T05:12:47ZengWileyIET Cyber-Physical Systems2398-33962024-06-019211512410.1049/cps2.12089Securing the Internet of Medical Things with ECG‐based PUF encryptionBiagio Boi0Christian Esposito1University of Salerno Salerno ItalyUniversity of Salerno Salerno ItalyAbstract The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource‐constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre‐processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context.https://doi.org/10.1049/cps2.12089ElectroCardioGram (ECG) Based EncryptionInternet of Medical Things (IoMT)Physical Unclonable Function (PUF) |
| spellingShingle | Biagio Boi Christian Esposito Securing the Internet of Medical Things with ECG‐based PUF encryption IET Cyber-Physical Systems ElectroCardioGram (ECG) Based Encryption Internet of Medical Things (IoMT) Physical Unclonable Function (PUF) |
| title | Securing the Internet of Medical Things with ECG‐based PUF encryption |
| title_full | Securing the Internet of Medical Things with ECG‐based PUF encryption |
| title_fullStr | Securing the Internet of Medical Things with ECG‐based PUF encryption |
| title_full_unstemmed | Securing the Internet of Medical Things with ECG‐based PUF encryption |
| title_short | Securing the Internet of Medical Things with ECG‐based PUF encryption |
| title_sort | securing the internet of medical things with ecg based puf encryption |
| topic | ElectroCardioGram (ECG) Based Encryption Internet of Medical Things (IoMT) Physical Unclonable Function (PUF) |
| url | https://doi.org/10.1049/cps2.12089 |
| work_keys_str_mv | AT biagioboi securingtheinternetofmedicalthingswithecgbasedpufencryption AT christianesposito securingtheinternetofmedicalthingswithecgbasedpufencryption |