How Privacy-Enhanced Technologies (Pets) are Transforming Digital Healthcare Delivery
Keywords:Privacy enhanced technologies, Digital healthcare
Privacy Enhancing Technologies (PETs) are playing a crucial role in maturing digital healthcare delivery for mainstream adaption from both a social and regulatory perspective. Different PETs are improving different aspects of digital healthcare delivery, and we have chosen seven of them to observe in the context of their influence on digital healthcare and their use cases. Homomorphic encryption can provide data security when healthcare data is being collected from individuals via IoT or IoMT devices. It’s also a key facilitator for large-scale healthcare data pooling from multiple sources for analytics without compromising privacy. Secure Multi-Party Computation (SMPC) facilitates safe data transfer between patients and healthcare professionals, and other relevant entities. Generative Adversarial Networks (GANs) can be used to generate larger data sets from smaller training data sets directly obtained from the patients, to train AI and ML algorithms. Differential Privacy (DP) focuses on combining multiple data sets for collective or individual processing without compromising privacy. However, its addition of noise to obscure data has some technical limitations. Zero-Knowledge Proof (ZKP) can facilitate safe verifications/validation protocols to establish connections between healthcare devices without straining their hardware capacities. Federated learning leans quite heavily towards training AI/ML algorithms on multiple data sets without margining or compromising the privacy of the constituents of any dataset. Obfuscation can be used in different stages of healthcare delivery to obscure healthcare data.
J. Scheibner , J. L. Raisaro , J. R. Troncoso-Pastoriza , . M. Ienca, J. Fellay, E. Vayena and J. Hubaux, "Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis," JMIR Publications, 2021.
R. Bhatia and K. Munjal, "A systematic review of homomorphic encryption and its contributions in healthcare industry," Complex & Intelligent Systems, 2022.
A. Ali, M. F. Pasha, J. Ali, O. H. Fang, M. Masud, A. D. Jurcut and M. A. Alzain, "Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography," Sensors, 2022.
L. Vogelsang, M. Lehne, P. Schoppmann, P. Fabian , T. Sylvia , S. Björn and S. Josef , "A Secure Multi-Party Computation Protocol for Time-To-Event Analyses," in Digital Personalized Health and Medicine, 2020, pp. 8-12.
K.-L. Tan, C.-H. Chi, and K.-Y. Lam, "Secure Multi-Party Delegated Authorisation For Access and Sharing of Electronic Health Records," arXiv:2203.12837, 2022.
T. Amirsina and E. A. Fox, "CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records," in The Thirty-Third International FLAIRS Conference, Blacksburg, Virginia, 2020.
A. W. Huang, A. Kandula and X. Wang, "A Differential-Privacy-Based Blockchain Architecture to Secure and Store Electronic Health Records," ICBCT, 2021.
B. Sharma, R. Halder, and J. Singh, "Blockchain-based Interoperable Healthcare Using Zero-knowledge Proofs and Proxy Re-Encryption," in 2020 International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India, 2020.
Y. Zhang, D. He, Y. Li, M. Zhang, and K.-K. R. Choo, "Efficient Obfuscation for Encrypted Identity-Based Signatures in Wireless Body Area Networks," IEEE Systems Journal, 2020.
A. Sengupta and M. Rathor, "Structural Obfuscation and Crypto-Steganography-Based Secured JPEG Compression Hardware for Medical Imaging Systems," IEEE Access, 2020.
M. A. Will and R. K. Ko, "Chapter 5 - A guide to homomorphic encryption," The Cloud Security Ecosystem, pp. 101-127, 2015.
M. C. Compagnucci, J. Meszaros, T. Minssen, A. Arasilango, T. Ous, and M. Rajarajan, "Homomorphic Encryption: the 'Holy Grail' for Big Data Analytics & Legal Compliance in the Pharmaceutical and Healthcare Sector?," European Pharmaceutical Law Review, pp. 144-155, 2019.
A. E. Bouchti, S. Bahsani, and T. Nahhal, "Encryption as a service for data healthcare cloud security," in 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), New Delhi, India, 2020.
M. M. Salim, I. Kim, U. Doniyor, C. Lee and J. H. Park, "Homomorphic Encryption Based Privacy-Preservation for IoMT," Applied Sciences (MDPI), 2021.
O. Kocabas, T. Soyata, J.-P. Couderc, M. Aktas, J. Xia and M. Huang, "Assessment of cloud-based health monitoring using Homomorphic Encryption," in IEEE, Asheville, NC, 2013.
A. Prasitsupparote, Y. Watanabe and J. Shikata, "Implementation and Analysis of Fully Homomorphic Encryption in Wearable Devices," in ISDF, Greece, 2018.
Ö. Kocaba? and T. Soyata, "Medical Data Analytics in the Cloud Using Homomorphic Encryption," E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications, p. 18, 2016.
L. Zhang, J. Xu, P. Vijayakumar, P. K. Sharma, and U. Ghosh, "Homomorphic Encryption-based Privacy-preserving Federated Learning in IoT-enabled Healthcare System," IEEE Transactions on Network Science and Engineering, pp. 1 - 17, 2022.
X. Yue, H. Wang, D. Jin, M. Li, and W. Jiang, "Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control," Journal of Medical Systems, 2016.
R. Tso, A. Alelaiwi, S. M. M. Rahman, M.-E. Wu and M. S. Hossain, "Privacy-Preserving Data Communication Through Secure Multi-Party Computation in Healthcare Sensor Cloud," Journal of Signal Processing Systems volume, pp. 51-59, 2017.
B. Paria, A. Lahiri, and P. K. Biswas, "PolicyGAN: Training generative adversarial networks using policy gradient," in IEEE, Bangalore, India, 2017.
Z. Yu, Q. Xiang, J. Meng, C. Kou, Q. Ren, and Y. Lu, "Retinal image synthesis from multiple-landmarks input with generative adversarial networks," BioMedical Engineering OnLine, 29.
A. Bissoto, F. Perez, E. Valle, and S. Avila, "Skin Lesion Synthesis with Generative Adversarial Networks," Springer, 2018.
V. Saaran, V. Kushwaha, S. Gupta and G. Agarwal, "A Literature Review on Generative Adversarial Networks with Its Applications in Healthcare," Congress on Intelligent Systems, vol. Volume 1, pp. 215-225, 2021.
M. T. Zia, M. A. Khan, and H. El-Sayed, "Application of Differential Privacy Approach in Healthcare Data – A Case Study," in 14th International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, 2020.
A. Alnemari, C. J. Romanowski and R. K. Raj, "An Adaptive Differential Privacy Algorithm for Range Queries over Healthcare Data," in IEEE International Conference on Healthcare Informatics, Rochester, NY, USA, 2017.
A. Vadavalli and R. Subhashini, "An Improved Differential Privacy-Preserving Truth Discovery approach In Healthcare," in IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, Canada, 2019.
S. Kalaiyarasi and A. J. S. Kumar, "Secured Healthcare and Patient Monitoring System using Vanet Based Wban," in International Journal of Engineering Research & Technology (IJERT), Salem, India, 2015.
J. A. Chaudhry, S. Member, K. Saleem, M. Alazab, H. M. A. Zeeshan, J. Al-Muhtadi and J. J. P. C. Rodrigues, "Data Security Through Zero-Knowledge Proof and Statistical Fingerprinting in Vehicle-to-Healthcare Everything (V2HX) Communications," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 22, no. 6, pp. 3869 - 3879, 2021.
G. S. Gaba, M. Hedabou, P. Kumar, A. Braeken, M. Liyanage and M. Alazab, "Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare," Sustainable Cities and Society, vol. 80, 2022.
A. E. B. Tomaz, J. C. D. Nascimento, A. S. Hafid and J. N. D. Souza, "Preserving Privacy in Mobile Health Systems Using Non-Interactive Zero-Knowledge Proof and Blockchain," IEEE Access, vol. 8, pp. 204441-204458, 2020.
J. Li, Y. Meng, L. Ma, S. Du, H. Zhu, and Q. Pei, "A Federated Learning Based Privacy-Preserving Smart Healthcare System," IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2021-2031, March 2022.
A. Qayyum, K. Ahmad, M. A. Ahsan, A. Al-Fuqaha and J. Qadir, "Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge," IEEE Open Journal of the Computer Society, vol. 3, pp. 172-184, 2022.
L. H. Iwaya, F. Giunchiglia, L. A. Martucci, A. Hume, S. Fischer-Hübner and R. Chenu-Abente, "Ontology-Based Obfuscation and Anonymisation for Privacy," IFIP Advances in Information and Communication Technology, vol. 476, 2016.
K. Vatanparvar, V. Nathan, E. Nemati, M. M. Rahman and J. Kuang, "A Generative Model for Speech Segmentation and Obfuscation for Remote Health Monitoring," in IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks, Mountain View, CA, USA, 2019.
S. Xie, F. Zhang, and R. Cheng, "Security Enhanced RFID Authentication Protocols for Healthcare Environment," Wireless Personal Communications, vol. 117, p. 71–86, 2021.
A. Yala, V. Quach, H. Esfahanizadeh, R. G. L. D'Oliveira, K. R. Duffy, M. Médard, T. S. Jaakkola and R. Barzilay, "Syfer: Neural Obfuscation for Private Data Release," arXIV, 2022.
N. Cummins and B. W. Schuller, "Five Crucial Challenges in Digital Health," Frontiers in Digital Health, 2020.
How to Cite
Copyright (c) 2022 American Academic Scientific Research Journal for Engineering, Technology, and Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.