Key Management-Based Data Aggregation Secured with Privacy and Confidentiality-Preserving Scheme for Fog-Assisted WBANs

The intrinsic properties of wireless body area networks (WBANs) are examined in this research work, which highlights the fact that patient health-related data is extremely sensitive and should be protected. However, once a user’s secret key is compromised, the contents of encrypted information could be exposed and infringe on their right to privacy. Moreover, it is crucial for medical professionals (MP) to verify the integrity of encrypted data because correct statistical analysis of the collected data is always required for further services. To achieve this, secure key management-based data aggregation with privacy and confidentiality-preserving techniques for fog-assisted WBANs is proposed. It is based on the modified Boneh–Goh–Nissim (BGN) cryptosystem. It permits each fog node to collect first-level verified encrypted data from WBAN and transmit it to the medical server (MS) for further analysis and storage. Meanwhile, MS generates second-level verified encrypted aggregated data and delivers the results to MP after obtaining a list of monitoring health parameters from MS. The suggested scheme enables MP to verify the integrity of encrypted aggregated data and perform statistical analyses on the collected data without knowing the details of individual users, so that users’ privacy cannot be affected even if MP’s private key is hacked or disclosed. The security analysis demonstrates that it offers key security characteristics, and the performance analysis validates its statistical analysis, while also consuming low computational, and communication overhead relative to the relevant existing schemes. That is, to compute the statistical analysis of “n” health parameters, it consumes (n + 1) multiplication operations as a computational overhead, and consumes 11, 776 bits as its communication overhead to communicate 1 to n health parameters.