Abstract
An outbreak of infectious diseases does as much harm to human society as a war, and which is even more difficult to prevent. The prevention of infectious diseases needs to start from the source, meanwhile, to identify anyone in close contact. Finding the close contacts via Bluetooth search signals to better identify potential patients can help to reduce the impact on human society caused by droplet transmission of infectious diseases. In this study, we use Bluetooth search record and machine learning to identify close contacts on the premise of protecting patients’ privacy. We proposed a classification method based on privacy protection, which can store and calculate user’s private information without revealing it on an untrusted server, and without knowing the user’s plain text message of the saved information. This method can be carried out with acceptable computational overhead and achieve a higher accuracy.
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This work is supported by the National Natural Science Foundation of China (61872069), the Fundamental Research Funds for the Central Universities (N2017012).
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Wang, S., Wang, C., Xu, J. (2020). Exploiting Bluetooth to Enquire Close Contacts Without Privacy Leakage. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_31
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DOI: https://doi.org/10.1007/978-3-030-62223-7_31
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