EPIC: Efficient Privacy-Preserving Contact Tracing for Infection Detection

The world has experienced many epidemic diseases in the past, SARS, H1N1, and Ebola are some examples of these diseases. When those diseases outbreak, they spread very quickly among people and it becomes a challenge to trace the source in order to control the disease. In this paper, we propose an efficient privacy-preserving contact tracing for infection detection (EPIC) which enables users to securely upload their data to the server and later in case of one user got infected other users can check if they have ever got in contact with the infected user in the past. The process is done privately and without disclosing any unnecessary information to the server. Our scheme uses a matching score to represent the result of the contact tracing, and uses a weight-based matching method to increase the accuracy of the score. In addition, we have developed an adaptive scanning method to optimize the power consumption of the wireless scanning process. Further, we evaluate our scheme in real experiment and show that the user's privacy is preserved, and the accuracy achieves 93% in detecting the contact tracing based on the matching score in an energy efficient way.