  The practical utility of agent-based models in decision-making relies on
their capacity to accurately replicate populations while seamlessly integrating
real-world data streams. Yet, the incorporation of such data poses significant
challenges due to privacy concerns. To address this issue, we introduce a
paradigm for private agent-based modeling wherein the simulation, calibration,
and analysis of agent-based models can be achieved without centralizing the
agents attributes or interactions. The key insight is to leverage techniques
from secure multi-party computation to design protocols for decentralized
computation in agent-based models. This ensures the confidentiality of the
simulated agents without compromising on simulation accuracy. We showcase our
protocols on a case study with an epidemiological simulation comprising over
150,000 agents. We believe this is a critical step towards deploying
agent-based models to real-world applications.
