Dual Privacy-Preserving Health Data Aggregation Scheme Assisted by Medical Edge Computing

In the healthcare system, patients equipped with resource-limited medical devices generate a huge amount of health data. Collected from distributed medical devices, these health data has significant value for public health management. The data aggregation technique can be used to collect health data and effectively reduce communication bandwidth. However, the exiting health data aggregation schemes only retrieve the aggregated result, which constrains the usage of the aggregate function. Besides, there are some security and privacy issues, such as the disclosure of patients' identities, the attacks from malicious users, etc. In this paper, we propose a medical edge computing aided health data aggregation scheme with dual privacy preservation. Specially, the health data can be encoded to achieve recoverable health data aggregation by using Mykletun's algorithm. In addition, dynamic and traceable pseudonym technique is used to prevent the disclosure of patient identity after multiple data submissions. Furthermore, we use the reputation score to prevent attacks from malicious users. Meanwhile, fair incentive mechanism is adopted to stimulate patients to contribute their health data. To eliminate key escrow problem and improve the efficiency of authentication, the certificateless aggregate signature without pairing is utilized. Security analysis shows that our scheme can not only guarantee the privacy, confidentiality and integrity of health data, but also resist to the attacks from external and internal malicious users. Performance evaluation demonstrates that the computational cost of our scheme on the edge server and cloud server is superior to other schemes.