Differentially Private Modeling of Disease Transmission within Human Contact Networks
Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to vastly increase the scale at which such data can be collected. However, contact networks may include sensitive information, such as sexual relationships or drug use behavior. Protecting individual privacy while maintaining the scientific usefulness of the data is crucial. We propose a privacy-preserving pipeline for disease spread simulation studies based on a sensitive network that integrates differential privacy (DP) with statistical network models such as stochastic block models (SBMs) and exponential random gr...
| Authors | Shlomi Hod, Debanuj Nayak, Jason R. Gantenberg et al. |
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| Primary category | cs.CR |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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