Private agent-based modeling

Conference item

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.

Authors: Chopra, A; Quera-Bofarull, A; Giray-Kuru, N; Wooldridge, M; Raskar, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computing Machinery
• Host title: AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems
• Pages: 381-390
• Publication date: 2024-05-06
• Acceptance date: 2023-12-21
• Event title: 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
• Event location: Auckland, New Zealand
• Event website: https://www.aamas2024-conference.auckland.ac.nz/
• Event start date: 2024-05-06
• Event end date: 2024-05-10
• ISBN: 9798400704864
• Language: English
• Keywords: multi-party computation; automatic differentiation; privacy; differentiable agent-based modeling; epidemiology