Paper 2020/522

Privately Connecting Mobility to Infectious Diseases via Applied Cryptography

Alexandros Bampoulidis
Alessandro Bruni
Lukas Helminger
Daniel Kales
Christian Rechberger
Roman Walch

Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases. While prior studies have exclusively relied on a mobile network operator's subscribers' aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol's open-source implementation can process eight million subscribers in 70 minutes.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. Proceedings on Privacy Enhancing Technologies, 2022
homomorphic encryption COVID-19 mobile data secure computation differential privacy infectious diseases
Contact author(s)
lukas helminger @ iaik tugraz at
daniel kales @ iaik tugraz at
christian rechberger @ tugraz at
roman walch @ iaik tugraz at
2022-06-13: last of 3 revisions
2020-05-05: received
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Creative Commons Attribution


      author = {Alexandros Bampoulidis and Alessandro Bruni and Lukas Helminger and Daniel Kales and Christian Rechberger and Roman Walch},
      title = {Privately Connecting Mobility to Infectious Diseases via Applied Cryptography},
      howpublished = {Cryptology ePrint Archive, Paper 2020/522},
      year = {2020},
      note = {\url{}},
      url = {}
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