Lightweight Privacy-Preserving Medical Diagnostic Scheme for Internet of Things Healthcare

Remote medical diagnosis is an emerging trend in technological development. Retrieving diagnostic reports is crucial in remote medical diagnosis systems. However, patients’ medical records are highly sensitive and valuable for accurate disease diagnoses. Additionally, timely and accurate diagnostic reports are vital for efficient medical services. Therefore, timely and accurate diagnosis by healthcare professionals, while respecting patient privacy, is a challenging yet promising task. Hence, this work proposes a lightweight and efficient privacy-preserving medical diagnostic scheme based on Bloom filters and oblivious transfer protocol. The Bloom filter is employed in this scheme to compress the search space, thereby enhancing query speed. Additionally, the oblivious transfer protocol and polynomial encoding techniques are integrated to safeguard user query privacy and database privacy. Based on experimental results, our proposed solution significantly reduces matching time to the millisecond level and decreases communication overhead by 60%. Therefore, the proposed scheme is more suitable for medical diagnostic systems in the Internet of Things environment that require high time and privacy requirements.