A Cloud-Based Privacy-Preserving e-Healthcare System Using Particle Swarm Optimization

  • M. Swathi
  • K. C. SreedharEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)


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.


e-profile Health care Particle swarm optimization Symptoms Disease Cluster 


  1. 1.
    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.CrossRefGoogle Scholar
  2. 2.
    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.Google Scholar
  3. 3.
    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.Google Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    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.Google Scholar
  6. 6.
    Li, D., Q. Lv, L. Shang, and N. Gu. 2017. Efficient Privacy-Preserving Content Recommendation for Online Social Communities. Neurocomputing 219: 440–454.CrossRefGoogle Scholar
  7. 7.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Information TechnologySri Indu College of Engineering and TechnologyHyderabadIndia
  2. 2.Department of Computer Science and EngineeringSreenidhi Institute of Science and TechnologyHyderabadIndia

Personalised recommendations