A cloud-based privacy-preserving e-healthcare system using particle swarm optimization

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.