Private Agent-Based Modeling

TL;DR

This paper presents a privacy-preserving framework for agent-based modeling that enables decentralized simulation and analysis without exposing sensitive agent data, using secure multi-party computation techniques.

Contribution

It introduces a novel paradigm for private agent-based modeling that maintains accuracy while ensuring data confidentiality through secure computation protocols.

Findings

Successful implementation on a large epidemiological case study with over 150,000 agents

Demonstrates feasibility of privacy-preserving agent-based simulations in real-world scenarios

Provides protocols that prevent central data collection without sacrificing model fidelity

Abstract

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.