An Effective Method for Privacy Concerns on Digital Contact Tracing Protocols

In the post-epidemic era of normalized COVID-19, digital contact tracing will continue to be used as an efficient epidemiological investigation tool. Meanwhile, its widespread use has raised concerns about long-term data privacy and security. In this paper, an effective method for privacy concerns on digital contact tracing protocols (DCTP) is proposed. Digital contact tracing protocols are classified from both technical and architectural perspective. Then, the LINDDUN (each letter of "LINDDUN" stands for a privacy threat type) privacy threat modeling framework is used to analyze the privacy threats that may be contained in different protocols. Last, mitigation strategies are provided to advance privacy protection for future digital contact tracing protocols. The experiment show that the proposed method significantly outperforms existing methods.