A privacy-preserving target pattern matching scheme for digital health system
Shengnan Wang, Hua Shen · 2022
The traditional disease detection method is that a doctor judges if a user has a specific disease through the doctor's treatment based on his inference and medical detection reports provided by the medical equipment. In recent years, with the continuous development of digital healthcare, more and more users choose to conduct disease detection through a digital health system. To provide accurate disease detection service, the system needs to collect mass patients' medical information such as symptoms and signs. However, patients' medical information and users' query requests will reveal their privacy information, such as age, identification, address, physical condition. For the above issues, this paper proposes a privacy preserving target pattern matching scheme (PP-TPMS). Our scheme utilizes bloom filters and secret sharing technology to realize secure pattern matching between given query requests and mass medical information. The auxiliary diagnosis results are returned to users. The experimental results show that this scheme has efficient computation performance and communication performance.