Differential Privacy for Markov Chain State Trajectories

2026-08-08

AI Summary

This work addresses the privacy risks inherent in directly sharing state trajectories from Markov chains in data-driven systems, which can inadvertently reveal sensitive user information. To mitigate this, the paper proposes the first online differential privacy framework tailored specifically for Markov chain trajectories. The approach models trajectories as weighted directed graphs and integrates typical set theory with shortest-path optimization to generate high-fidelity synthetic trajectories under ε-differential privacy (with ε=3). The method substantially improves utility, achieving up to an 80% reduction in entropy compared to existing techniques under the same privacy budget, thereby effectively preserving both trajectory realism and essential statistical properties.

Abstract

Data-driven systems may require state trajectories of Markov chains to function because these trajectories contain information that is useful to the system, e.g., a product's credit risk, a user's physical location, or a user's internet browsing behavior. However, sharing such state trajectories can reveal sensitive information about users, which presents a privacy threat. Therefore, we develop a new framework for privatizing the state trajectories in a Markov chain using differential privacy. Our framework privatizes state trajectories online, in the sense that a private state trajectory is generated at the same time as the sensitive one it approximates. We treat Markov chains as weighted directed graphs whose edge weights are the negative logarithms of the transition probabilities. Then, each state in a private state trajectory is chosen by minimizing its distance to the corresponding state in the sensitive state trajectory, where the notion of distance is equal to the total edge weight along a shortest path. We prove that with high probability the private state trajectory remains close to the sensitive one, which maintains high utility for downstream uses of private data. Additionally, we prove that private state trajectories are consistently in the typical set of state trajectories generated by the underlying Markov chain, which means that private state trajectories have similar statistical properties to actual state trajectories produced by the underlying Markov chain. Numerical simulations show that under $3$-differential privacy, the mechanism we introduce exhibits up to an $80\%$ decrease in entropy compared to the state of the art, which illustrates that private state trajectories generated by our framework more closely resemble their corresponding sensitive state trajectory while maintaining the same level of privacy.

Problem

• Differential Privacy
• Markov Chain
• State Trajectories
• Privacy Preservation
• Sensitive Data

Innovation

• Differential Privacy
• Markov Chain
• State Trajectory
• Online Privacy
• Typical Set