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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.12983 |
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| _version_ | 1866913322268360704 |
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| author | Chopra, Ayush Quera-Bofarull, Arnau Giray-Kuru, Nurullah Wooldridge, Michael Raskar, Ramesh |
| author_facet | Chopra, Ayush Quera-Bofarull, Arnau Giray-Kuru, Nurullah Wooldridge, Michael Raskar, Ramesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12983 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Private Agent-Based Modeling Chopra, Ayush Quera-Bofarull, Arnau Giray-Kuru, Nurullah Wooldridge, Michael Raskar, Ramesh Multiagent Systems Cryptography and Security Social and Information Networks 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. |
| title | Private Agent-Based Modeling |
| topic | Multiagent Systems Cryptography and Security Social and Information Networks |
| url | https://arxiv.org/abs/2404.12983 |