SymMatch: Secure and privacy-preserving symptom matching for mobile healthcare social networks

Recently, mobile healthcare social networks (MHSN), serving as a promising platform for eHealthcare system, has attracted considerable interest. Profile matching is an effective method for medical users to find possible helpers in the mobile healthcare social networks while preserving privacy of the personal health information (PHI). This paper concerns the challenging problem of how to resist inside semi-honest-but-curious medical users from disclosing the PHI by altering their symptom matching results and joining the data transmission group. A new security threat arising from existing profile matching protocols, coined as inflation attack, is first formulated. Then, a new secure and privacy-preserving symptom matching scheme, called SymMatch, is proposed based on homomorphic encryption to thwart the inflation attack and other active attacks. The security analysis and simulation results show the effectiveness and efficiency of the proposed scheme.