Paper 2023/1779

Privacy-Preserving Cross-Facility Early Warning for Unknown Epidemics

Shiyu Li, University of Electronic Science and Technology of China
Yuan Zhang, University of Electronic Science and Technology of China
Yaqing Song, University of Electronic Science and Technology of China
Fan Wu, Central South University
Feng Lyu, Central South University
Kan Yang, The University of Memphis
Qiang Tang, The University of Sydney

Syndrome-based early epidemic warning plays a vital role in preventing and controlling unknown epidemic outbreaks. It monitors the frequency of each syndrome, issues a warning if some frequency is aberrant, identifies potential epidemic outbreaks, and alerts governments as early as possible. Existing systems adopt a cloud-assisted paradigm to achieve cross-facility statistics on the syndrome frequencies. However, in these systems, all symptom data would be directly leaked to the cloud, which causes critical security and privacy issues. In this paper, we first analyze syndrome-based early epidemic warning systems and formalize two security notions, i.e., symptom confidentiality and frequency confidentiality, according to the inherent security requirements. We propose EpiOracle, a cross-facility early warning scheme for unknown epidemics. EpiOracle ensures that the contents and frequencies of syndromes will not be leaked to any unrelated parties; moreover, our construction uses only a symmetric-key encryption algorithm and cryptographic hash functions (e.g., [CBC]AES and SHA-3), making it highly efficient. We formally prove the security of EpiOracle in the random oracle model. We also implement an EpiOracle prototype and evaluate its performance using a set of real-world symptom lists. The evaluation results demonstrate its practical efficiency.

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eHealth systemssyndrome-based early epidemic warningprivacy preservation
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Shai_Li @ yeah net
ZY_LoYe @ 126 com
YaqingS @ 163 com
wfwufan @ csu edu cn
fenglyu @ csu edu cn
kan yang @ memphis edu
qiang tang @ sydney edu au
2023-12-04: revised
2023-11-17: received
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      author = {Shiyu Li and Yuan Zhang and Yaqing Song and Fan Wu and Feng Lyu and Kan Yang and Qiang Tang},
      title = {Privacy-Preserving Cross-Facility Early Warning for Unknown Epidemics},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1779},
      year = {2023},
      note = {\url{}},
      url = {}
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