# Differentially Private Modeling of Disease Transmission within Human Contact Networks

arXiv: https://arxiv.org/abs/2604.07493 · HTML: https://mlsanity.com/paper/2604.07493/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 111 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Node-Level Differentially Private Graph Neural Networks](https://mlsanity.com/paper/2111.15521/) (2021)
- [Secure Deep Graph Generation with Link Differential Privacy](https://mlsanity.com/paper/2005.00455/) (2020)
- [Private Graphon Estimation for Sparse Graphs](https://mlsanity.com/paper/1506.06162/) (2015)
- [Differentially Private Release of Israel's National Registry of Live Births](https://mlsanity.com/paper/2405.00267/) (2024)
- [Recursive Mechanism: Towards Node Differential Privacy and Unrestricted Joins [Full Version, Draft 0.1]](https://mlsanity.com/paper/1304.4795/) (2013)
- [Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation](https://mlsanity.com/paper/1810.02183/) (2018)
- [Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy](https://mlsanity.com/paper/1905.10477/) (2019)
- [Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)](https://mlsanity.com/paper/2004.04145/) (2020)
- [PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information](https://mlsanity.com/paper/2304.02401/) (2023)
- [Detecting Communities under Differential Privacy](https://mlsanity.com/paper/1607.02060/) (2016)
- [Differentially Private Exponential Random Graphs](https://mlsanity.com/paper/1409.4696/) (2014)
- [Differential Privacy from Locally Adjustable Graph Algorithms: k-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs](https://mlsanity.com/paper/2211.10887/) (2022)
- [Privacy Attacks on Network Embeddings](https://mlsanity.com/paper/1912.10979/) (2019)
- [Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models](https://mlsanity.com/paper/1511.02930/) (2015)
- [Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy](https://mlsanity.com/paper/2212.04133/) (2022)
- [Node-Differentially Private Estimation of the Number of Connected Components](https://mlsanity.com/paper/2304.05890/) (2023)
- [PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy](https://mlsanity.com/paper/2501.02354/) (2025)
- [Differentially-Private Two-Party Egocentric Betweenness Centrality](https://mlsanity.com/paper/1901.05562/) (2019)
- [Community Preserved Social Graph Publishing with Node Differential Privacy](https://mlsanity.com/paper/2101.01450/) (2021)
- [Network Generation with Differential Privacy](https://mlsanity.com/paper/2111.09085/) (2021)
- [PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics](https://mlsanity.com/paper/2412.11369/) (2024)
- [Privately Estimating Graph Parameters in Sublinear time](https://mlsanity.com/paper/2202.05776/) (2022)
- [Differentially Private Computation of Basic Reproduction Numbers in Networked Epidemic Models](https://mlsanity.com/paper/2309.17284/) (2023)
- [Scalable Distributed Reproduction Numbers of Network Epidemics with Differential Privacy](https://mlsanity.com/paper/2501.18862/) (2025)
- [Controlling the Spread of Epidemics on Networks with Differential Privacy](https://mlsanity.com/paper/2506.00745/) (2025)

## Abstract (arXiv)

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 graph models (ERGMs). Our pipeline comprises three steps: (1) compute network summary statistics using node-level DP (which corresponds to protecting individuals' contributions); (2) fit a statistical model, like an ERGM, using these summaries, which allows generating synthetic networks reflecting the structure of the original network; and (3) simulate disease spread on the synthetic networks using an agent-based model. We evaluate the effectiveness of our approach using a simple Susceptible-Infected-Susceptible (SIS) disease model under multiple configurations. We compare both numerical results, such as simulated disease incidence and prevalence, as well as qualitative conclusions such as intervention effect size, on networks generated with and without differential privacy constraints. Our experiments are based on egocentric sexual network data from the ARTNet study (a survey about HIV-related behaviors). Our results show that the noise added for privacy is small relative to other sources of error (sampling and model misspecification). This suggests that, in principle, curators of such sensitive data can provide valuable epidemiologic insights while protecting privacy.