Toward Privacy-Preserving Directly Contactable Symptom-Matching Scheme for IoT Devices

The development of IoT devices has driven technological advancements across industries, especially in healthcare. IoT devices have brought many conveniences to patients, such as symptom matching, the real-time acquisition of health data, and online diagnosis. However, the development of the Internet of Things also brings security and privacy challenges, which have attracted the attention of many scholars. In symptom matching, patients can communicate with patients similar to themselves through symptom matching, exchange treatment experiences, and encourage each other. However, matching in plaintext will pose a huge threat to user privacy, such as discrimination, which in turn affects job hunting, etc. Therefore, this paper proposes a symptom-matching scheme for IoT devices based on the Diffie–Hellman key agreement. Specifically, we construct and formally define the Switching Threshold Label Private Set Intersection (STLPSI) protocol based on the Diffie–Hellman key agreement and apply it for medical symptom matching. Our protocol can not only set the threshold of the same symptoms, but also patients who meet the threshold can obtain one another’s contact information. Furthermore, our scheme does not rely on any trusted third parties. Through security analysis and experiments, our scheme is shown to be effective in preserving privacy during symptom matching.