An optimal cloud-based e-healthcare system using k-centroid MVS clustering scheme

• Published in: Journal of intelligent & fuzzy systems 2018-01, Vol.34 (3), p.1595-1607
• Main Authors: Sreedhar, K.C., Suresh Kumar, N.
• Format: Article
• Language: English
• Subjects: Clustering; Collaboration; Health care; Patients; Privacy; Similarity
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-169454

Because of its increasing usage, internet has become an integral component of our daily lives. In this paradigm, users can share their perceptions and collaborate with others easily through social communities. The e-healthcare community service is particularly recommended by individual patients who are remotely located, have embarrassing medical conditions, or have caretaker responsibilities that may prohibit them from obtaining satisfactory face-to-face medical and emotional support. However, participation in such online social collaborations may be constrained 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 helps them to chat with other patients with similar problems, understand their feelings, and much more. However, patients’ private and sensitive information cannot be disclosed to anyone at any point of time. The recommended model uses a special technique, k-centroid multi-view point similarity algorithm, to cluster e-profiles based on their similarities. Finally, a distributed hashing technique is used to encrypt the clustered profiles to persevere patients’ personal information. The suggested framework is compared with well-known privacy-preserving clustering algorithms to compute accuracy and latency by using popular similarity measures.