Secure and Privacy-Preserving Agent-Based Modeling for Real-World Applications

Conceptos Básicos

This work introduces a paradigm for conducting agent-based simulations, calibration, and analysis in a privacy-preserving manner, enabling the practical deployment of agent-based models in real-world applications.

Resumen

The paper presents a framework for privacy-preserving agent-based modeling (ABM) that supports simulation, calibration, and analysis of ABMs without compromising the confidentiality of individual agents' sensitive information.
Key highlights:

The authors identify the need for privacy-preserving ABMs as the adoption of ABMs in real-world applications is hindered by the requirement to access granular microdata about the underlying agent population.
They leverage techniques from secure multi-party computation (MPC) to design protocols that enable decentralized computation in ABMs, ensuring the confidentiality of simulated agents without compromising simulation accuracy.
The proposed protocols support key ABM operations, including agent state updates, collection of aggregate statistics, and gradient-based calibration, while preserving privacy.
The authors demonstrate the capabilities of their approach through a case study on an epidemiological ABM for the city of Oxford, showcasing how privacy-preserving ABMs can provide the same level of insight as traditional ABMs.
The work constitutes the first framework for privacy-preserving ABMs, paving the way for the secure and practical utilization of ABMs as valuable tools for policy-making in real-world settings.

Estadísticas

The epidemiological ABM simulates a population of over 150,000 agents.
The contact graph for the city of Oxford is used to determine the neighborhood of each agent.
The baseline parameter values are: effective contact rate β = 0.5 day^-1, recovery rate γ = 0.1 day^-1, initial infected fraction I_0 = 0.01, and simulation duration of 60 days.

Citas

"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."

Ideas clave extraídas de

Private Agent-Based Modeling

by Ayush Chopra... a las arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12983.pdf

Consultas más profundas

How can the proposed privacy-preserving protocols be extended to support more complex contagion models that capture higher-order network effects

To extend the proposed privacy-preserving protocols to support more complex contagion models that capture higher-order network effects, several key considerations need to be taken into account. One approach is to incorporate multi-layer network structures that account for interactions beyond immediate neighbors. This can be achieved by modifying the message passing mechanism in the SecureAgentUpdate protocol to include information from neighbors of neighbors, creating a higher-order network effect. By expanding the neighborhood information considered in the simulation, agents can capture indirect influences and cascading effects within the network.
Additionally, the protocol can be adapted to handle dynamic network structures where connections between agents evolve over time. This dynamic aspect is crucial for modeling contagion spread in scenarios where network links change based on agent interactions or external factors. By introducing mechanisms for updating network connections securely and efficiently, the privacy-preserving ABM framework can accommodate the evolving nature of contagion models.
Furthermore, incorporating temporal dynamics into the simulation can enhance the model's ability to capture the spread of contagion over time. By integrating time-dependent factors into the agent update rules and message passing protocols, the framework can simulate the progression of contagion through complex networks with higher-order effects. This temporal dimension adds another layer of realism to the model, enabling more accurate representation of contagion dynamics in real-world scenarios.
In summary, extending the privacy-preserving protocols to support more complex contagion models with higher-order network effects involves incorporating multi-layer network structures, dynamic network evolution, and temporal dynamics into the simulation framework. By enhancing the model's ability to capture indirect interactions, evolving connections, and temporal dependencies, the framework can provide a more comprehensive representation of contagion spread in complex systems.

What are the practical engineering challenges in implementing the privacy-preserving ABM framework, such as minimizing communication overhead, supporting asynchronous message passing, and leveraging distributed computing

Implementing the privacy-preserving ABM framework poses several practical engineering challenges that need to be addressed to ensure efficient and effective operation. Some of the key challenges include:

Minimizing Communication Overhead: One of the primary challenges is reducing the communication overhead associated with secure multi-party computation (MPC). Efficient communication protocols and data transmission techniques need to be implemented to minimize the latency and bandwidth requirements of exchanging information between agents and the central server. Optimizing the communication protocols can help streamline the secure computation process and improve the overall performance of the framework.

Supporting Asynchronous Message Passing: Asynchronous message passing is essential for handling real-time interactions and updates in decentralized systems. Ensuring that the privacy-preserving protocols can accommodate asynchronous communication between agents and the central server is crucial for maintaining the responsiveness and scalability of the ABM framework. Implementing mechanisms for handling out-of-order messages and asynchronous updates can enhance the robustness and flexibility of the system.

Leveraging Distributed Computing: Leveraging distributed computing resources can help distribute the computational load across multiple nodes, improving scalability and performance. Implementing parallel processing techniques and distributed algorithms can enable efficient execution of privacy-preserving computations in large-scale ABMs. By harnessing the power of distributed computing, the framework can handle complex simulations with millions of agents while maintaining privacy and security.

Addressing these practical engineering challenges requires a combination of efficient communication protocols, asynchronous message handling mechanisms, and distributed computing strategies. By overcoming these challenges, the privacy-preserving ABM framework can achieve high performance, scalability, and responsiveness in modeling complex systems while safeguarding individual privacy.

How can the privacy-preserving ABM framework be combined with federated learning approaches to further enhance its capabilities and applicability

Combining the privacy-preserving ABM framework with federated learning approaches can offer synergistic benefits and enhance the capabilities and applicability of the model in various domains. Federated learning enables collaborative model training across distributed data sources without sharing raw data, aligning well with the privacy-preserving principles of the ABM framework. Here are some ways in which the two approaches can be integrated:

Privacy-Preserving Model Aggregation: Federated learning can be used to aggregate model updates from individual agents in a privacy-preserving manner. Each agent trains a local model using its private data and shares only the model parameters or gradients with a central server for aggregation. By applying secure aggregation techniques from federated learning to the privacy-preserving ABM framework, the model can be updated collaboratively without compromising individual privacy.

Distributed Model Training: Federated learning allows for distributed model training across multiple agents, enabling the privacy-preserving ABM framework to leverage the computational resources of diverse entities. By distributing the training process and aggregating local updates securely, the model can benefit from the collective intelligence of the distributed network while maintaining data privacy and confidentiality.

Enhanced Generalization and Robustness: Federated learning promotes model generalization and robustness by training on diverse data sources. By combining federated learning with the privacy-preserving ABM framework, the model can capture a broader range of scenarios and population dynamics, leading to more robust and accurate simulations. The collaborative nature of federated learning can help address data heterogeneity and improve the model's predictive capabilities.

Integrating federated learning with the privacy-preserving ABM framework can unlock new opportunities for collaborative modeling, data sharing, and knowledge transfer while upholding individual privacy rights. By leveraging the strengths of both approaches, the combined framework can enhance model performance, scalability, and adaptability in real-world applications across various domains.