Private Agent-Based Modeling

• URL: http://arxiv.org/abs/2404.12983v1
• Date: Fri, 19 Apr 2024 16:30:40 GMT
• Title: Private Agent-Based Modeling
• Authors: Ayush Chopra, Arnau Quera-Bofarull, Nurullah Giray-Kuru, Michael Wooldridge, Ramesh Raskar,
• Abstract summary: The utility of agent-based models in decision-making relies on their capacity to accurately replicate populations. Yet, the incorporation of such data poses significant challenges due to privacy concerns. 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.
• Score: 13.072333113108531
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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