🤖 AI Summary
This study investigates the equilibrium bid-ask spread and welfare allocation in the Glosten-Milgrom market-making model when trade direction signals are perturbed by binary flipping noise—serving as a proxy for differential privacy mechanisms. By extending the concept of privacy subsidies from continuous Gaussian settings to a discrete two-state market microstructure, and combining information-theoretic modeling with Bayesian equilibrium analysis, the authors derive a closed-form solution: the equilibrium spread equals μ(1−2η)Δ, and each trade entails a privacy subsidy of μηΔ transferred from liquidity providers to informed traders. These findings demonstrate the universal impact of privacy-preserving perturbations within classical market models and provide a theoretical foundation for the joint optimization of privacy protection and market design.
📝 Abstract
We derive a closed-form bid-ask spread and welfare decomposition for the Glosten-Milgrom 1985 sequential-trading model when the market maker observes the trade direction perturbed by a binary flip channel of probability $η$ -- a natural information-theoretic model of privacy mechanisms acting on the direction signal. Under a committed Bayesian market-maker pricing rule, the equilibrium spread is $μ(1-2η)Δ$, where $μ$ is the informed-trader fraction and $Δ= v_H - v_L$ the value range. The welfare decomposition identifies a per-trade transfer $μηΔ$ from the protocol's liquidity pool to traders -- the "privacy subsidy", mirroring the Gaussian-Kyle analog established in prior work. The result extends the privacy-subsidy concept from continuous Gaussian to discrete two-state microstructure, demonstrating robustness across both classical models. Primary application: MPC-based matching engines with $\varepsilon$-differentially-private direction disclosure, where the engine prices on a noisy direction signal.