(a,k)-Anonymous Scheme for Privacy-Preserving Data Collection in IoT-based Healthcare Services Systems

The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a1,k1)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a1,k1)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a2,k2)-anonymity (a1 ≥ a2, k2 ≥ k1). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality.