The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation
Open accessAbstract
Secure Multi-Party Computation (SMC) is a thriving strategy for privacy-preserving data sharing in the healthcare domain. This research examined the role of SMC in the healthcare context and its alignment with regulations such as HIPAA and GDPR. The study highlights key findings in advanced cryptographic techniques, usability enhancements, scalability improvements, as well as security and privacy assurance protocols within SMC. The potential implications of SMC on patient privacy healthcare data management are unquestionable in terms of protecting sensitive information, securing collaboration, and facilitating data-driven decision-making. This study demonstrates that SMC has the potential to revolutionize and transform healthcare by affirming privacy while facilitating secure data sharing, leading to enhanced healthcare outcomes and empowering patients with control over their data.
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“The implementation of robust privacy-preserving methods, like differential privacy and secure multiparty computation, is important but often enhances computational overhead and complexity to the system [111].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [111]
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Citations
Cites
Hasan, M. R., & Ferdous, J. (2024) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2024.6.1.10 · Cited by 70
Privacy-preserving self-serviced medical diagnosis scheme based on secure multi-party computation
Li, D., Liao, X., Xiang, T., Wu, J., & Le, J. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101701 · Cited by 61
Privacy Preserving Multiparty Multilevel DRM Architecture
Sachan, A., Emmanuel, S., Das, A., & Kankanhalli, M. (2009) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2009.4784937 · Cited by 39
Privacy-preserving record linkage in large databases using secure multiparty computation
Laud, P., & Pankova, A. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0400-8 · Cited by 32
Applying secure multi-party computation to improve collaboration in healthcare cloud
Marwan, M., Kartit, A., & Ouahmane, H. (2016) · doi:10.1109/sysco.2016.7831325 · Cited by 21
Toward Secure Clustered Multi-Party Computation: A Privacy-Preserving Clustering Protocol
Abbasi, S., Cimato, S., & Damiani, E. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36818-9_49 · Cited by 2
Research works printed but not linked: 9
- Chen, Y., Wang, Q., & Li, X. (2018). Differential Privacy for Electronic Health Records Sharing: A Case Study in Healthcare Data Protection. Journal of Medical Systems, 18(2), 45-60.
- Hasan, M. R. (2024). Revitalizing the Electric Grid: A Machine Learning Paradigm for Ensuring Stability in the USA. Journal of Computer Science and Technology Studies, 6(1), 141-154.
- Hasan, M. R. (2022). Cybercrime Techniques in Online Banking. Journal of Aquatic Science. Retrieved from https://www.journal- aquaticscience.com/article_158883.html
- Lee, S., Kim, E., & Park, H. (2020). Secure Aggregation Techniques for Health Monitoring Systems: A Simulation Study. IEEE Transactions on Information Technology in Biomedicine, 22(3), 112-127.
- ISACA. (2023). Privacy-Preserving analytics and secure multiparty computation. Retrieved from: https://www.isaca.org/resources/isaca- journal/issues/2021/volume-2/privacy-preserving-analytics-and-secure-multiparty-computation.
- Sarahneh, S. (2022). Secure data sharing policies and architecture preserving privacy. The 7th International Conference on Information Technology. Retrieved from: www.academia.edu. https://www.academia.edu/82572045/Secure_Data_Sharing_Polices_and_Architecture_Preserving_Privacy
- Sarahneh, S. (2022b). Secure Data Sharing Model for Preserving Privacy 1. Journal of Theoretical and Applied Information Technology, 95 (20). Retrieved from: www.academia.edu. https://www.academia.edu/82572063/Secure_Data_Sharing_Model_for_Preserving_Privacy_1
- Smith, J., Johnson, L., & Brown, A. (2019). Homomorphic Encryption for Genomic Data Sharing in Cloud Computing: A Case Study. Journal of Biomedical Informatics, 10(4), 325-340.
- Xavier, F. (2023). Secure data transactions for the multiparty processing system. International Journal of Computer Technology & Applications, 4 (2),324-326https://www.academia.edu/96147994/Secure_Data_Transaction_for_Multiparty_Processing_System.
Cited by
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf329 · Cited by 30
Metadata
- Authors
- Ahammed, M. F., & Labu, M. R.
- Affiliations
- Gannon University (US)
- Venue
- Journal of Medical and Health Studies
- Published
- 2024-04-07
- Volume
- 5
- Issue
- 2
- Pages
- 37-47
- ISSN
- 2710-1452 (ISSN-L)
- OpenAlex
- W4394063522
- Cited by
- 25
- References
- 6
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 1
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Medical and Health Studies (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- secure multi-party computation, privacy-preserving data sharing, healthcare data privacy, HIPAA, GDPR, cryptographic techniques, scalability, patient data control, data-driven decision making
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