Abstract
Internet has become an integral element of our daily lives owing to its increasing usage. During this model, users will share their information and collaborate with others simply through social communities. The e-healthcare community service significantly resolves the issues of individual patients who are remotely situated, have embarrassing medical conditions, or have caretaker responsibilities which will prohibit them from getting satisfactory face-to-face medical and emotional support. Participation in online social collaborations may not be easy due to cultural and language barriers. This paper proposes a privacy-preserving collaborative e-healthcare system that connects and integrates patients or caretakers into different groups. This system enables patients or caretakers to chat with other patients with similar problems, understand their feelings, and share many issues of their own. But during this process, private and sensitive information cannot be disclosed to anyone at any point of time. The recommended model uses a special technique, particle swarm optimization to cluster e-profiles based on their similarities. Finally, clustered profiles are encrypted using distributed hashing technique to persevere patients’ personal information. The results of proposed framework are compared with well-known privacy-preserving clustering algorithms by using popular similarity measures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sreedhar, K.C., M.N. Faruk, and B. Venkateswarlu. 2017. A Genetic TDS and BUG With Pseudo-Identifier for Privacy Preservation Over Incremental Data Sets. Journal of Intelligent and Fuzzy Systems 32 (4): 2863–2873.
Upmanyu, M., A.M. Namboodiri, K. Srinathan, and C.V. Jawahar, Efficient Privacy Preserving k-means Clustering. In: PAISI’10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics, 154–166.
Satish, M. and M. Ramakrishna Murt. 2015. Clustering with Mapreduce using Hadoop Framework. International Journal on Recent and Innovation Trends in Computing and Communication. 3(1): 409–413, ISSN 2321-8169.
Sreedhar, K.C., and N. Suresh Kumar 2018. An Optimal Cloud-Based e-healthcare System using k- Centroid MVS Clustering Scheme. Journal of Intelligent and Fuzzy Systems 34: 1595–1607.
Cui, Xiaohui, Thomas E. Potok, and Paul Palathingal. 2005. Document Clustering using Particle Swarm Optimization. In Proceedings 2005 IEEE Swarm Intelligence Symposium. SIS 2005. 0-7803-8916-6/05.
Li, D., Q. Lv, L. Shang, and N. Gu. 2017. Efficient Privacy-Preserving Content Recommendation for Online Social Communities. Neurocomputing 219: 440–454.
https://github.com/deshanadesai/Symptom-X-/blob/master/dataset_clean1.csv#L2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swathi, M., Sreedhar, K.C. (2020). A Cloud-Based Privacy-Preserving e-Healthcare System Using Particle Swarm Optimization. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)