Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy

2026-06-12

AI Summary

This study addresses the vulnerability of public testimony processes in independent redistricting commissions to adversarial manipulation and their limited capacity to effectively incorporate Communities of Interest (COI) into districting plans. To mitigate these issues, the work introduces differential privacy into COI-based redistricting for the first time, proposing two scoring functions amenable to optimization via Markov Edge-Walk (MEW). By employing the exponential mechanism, the method samples redistricting plans from a distribution that satisfies differential privacy, thereby integrating public input while resisting malicious submissions. Experiments on Missouri’s mid-decade redistricting demonstrate that the approach significantly outperforms uninformed baselines and existing methods. Adversarial evaluations further show that, under reasonable privacy budgets, the framework achieves both robustness and practical utility, with stronger COI preservation yielding more equitable representation for minority groups and Democratic voters.

Abstract

Independent Redistricting Commissions (IRCs) are a promising tool for bottom-up redistricting, but their public testimony processes are vulnerable to adversarial manipulation. We propose using differential privacy to draw redistricting plans that incorporate community of interest (COI) testimonies while remaining robust to adversarial input. Treating individual testimonies as data points, we use the marked edge walk to sample from differentially private distributions of redistricting plans via the exponential mechanism. We introduce two score functions and demonstrate that both can be targeted by MEW across a range of privacy budgets. Applying this method to Missouri's mid-cycle redistricting using 808 COI testimonies, we show that COI-informed sampling outperforms an uninformed baseline and the enacted plan. An adversarial experiment demonstrates that the method can be robust to attacks under certain privacy budgets and may perform better in practice than formal group privacy guarantees imply. We also find that stronger COI preservation tends to spread minority and Democratic representation more evenly across districts.

Problem

• redistricting
• communities of interest
• differential privacy
• adversarial manipulation
• independent redistricting commissions

Innovation

• differential privacy
• redistricting
• communities of interest
• marked edge walk
• exponential mechanism