The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems
Abstract
In IoT-based healthcare, medical devices are more vulnerable to numerous security threats and attacks than other network devices. Current solutions are able to provide protection to patients' data during data transmission to some extent, but cannot prevent some sophisticated threats and attacks such as collusion attacks and data leakage. In this article, we first investigate the challenges with privacy protected data collection. Then we propose a practical framework called PrivacyProtector, patient privacy protected data collection, with the objective of preventing these types of attacks. PrivacyProtector includes the ideas of secret sharing and share repairing (in case of data loss or compromise) for patients' data privacy. Since it is the first time, we apply the Slepian- Wolf-coding-based secret sharing (SW-SSS) in PrivacyProtector. In the framework, we use a distributed database consisting of multiple cloud servers, which ensures that the privacy of patients' personal data can remain protected as long as one of the servers remains uncompromised. We also present a patient access control scheme in which multiple cloud servers collaborate in shared construction to offer patients' data to healthcare providers without revealing the content of the data. The privacy performance analysis has shown that the PrivacyProtector framework is secure and privacy-protected against various attacks.
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Cites
Noiseless coding of correlated information sources
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Secure Deduplication with Efficient and Reliable Convergent Key Management
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Deploying Wireless Sensor Networks with Fault-Tolerance for Structural Health Monitoring
Bhuiyan, M. Z. A., Wang, G., Cao, J., & Wu, J. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2013.195 · Cited by 189
Self-citationDeploying Wireless Sensor Networks with Fault Tolerance for Structural Health Monitoring
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Self-citationPrivacy Protection for Wireless Medical Sensor Data
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A k-anonymity privacy-preserving approach in wireless medical monitoring environments
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Channel Simulation and Coded Source Compression
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Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection
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Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey
Sun, Y., Lo, F. P.-W., & Lo, B. (2019) · IEEE Access · doi:10.1109/access.2019.2960617 · Cited by 292
IoMT amid COVID-19 pandemic: Application, architecture, technology, and security
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237
Secure Healthcare Data Aggregation and Transmission in IoT—A Survey
Ullah, A., Azeem, M., Ashraf, H., Alaboudi, A. A., Humayun, M., & Jhanjhi, N. (2021) · IEEE Access · doi:10.1109/access.2021.3052850 · Cited by 213
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Metadata
- Authors
- Luo, E., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Atiquzzaman, M.
- Venue
- IEEE Communications Magazine
- Published
- 2018-02
- Volume
- 56
- Issue
- 2
- Pages
- 163-168
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2786190064
- Cited by
- 266
- References
- 11
- FWCI
- 60.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Luo, E., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Atiquzzaman, M. (2018). PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2018.1700364
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