Security and Privacy for Mobile Healthcare Networks

This book examines state-of-art research on designing healthcare applications with the consideration of security and privacy. It explains the Mobile Healthcare Network (MHN) architecture and its diverse applications, and reviews the existing works on security and privacy for MHNs. Critical future challenges and research problems are also identified.

Using a Quality-of-Protection perspective, the authors provide valuable insights on security and privacy preservation for MHNs. Some promising solutions are proposed to accommodate the issues of secure health data transmission, misbehavior detection, health data processing with privacy preservation and access control in MHNs. Specifically, the secure health data aggregation explores social spots to help forward health data and enable users to select the optimal relay according to their social ties and health data priority. The secure aggregation achieves the desirable delivery ratio with reasonable communication costs and lower delay for the data in different priorities.

A proposed misbehavior detection scheme distinguishes Sybil attackers from normal users by comparing their mobile contacts and pseudonym changing behaviors. The detection accuracy is high enough to resist various Sybil attacks including forgery. In addition, the health data processing scheme can analyze the encrypted health data and preserve user's privacy at the same time. Attribute based access control can achieve fine-grained acces

s control with user-defined access policy in MHNs. Security and Privacy for Mobile Healthcare Networks is designed for researchers and advanced-level students interested in healthcare security and secure data transmission.

Proposes a mobile healthcare network which enables efficient and secure health information collection, sharing and processing Addresses the privacy leakage issue when health data are processed in an untrusted cloud server under MHN architecture Investigates attribute based control access, semi-supervised learning with hidden Markov model, and cryptographic schemes

Inhalt
Introduction.- Security and Privacy Challenges in MHN.- Secure Health Data Collection in MHN.- Health Data Sharing with Misbehavior Detection.- Privacy-preserving Health Data Processing.- Access Control for MHN.- Summary and Future Research Directions.