English

The Privacy Subsidy: Kyle's $\lambda$ under Noise-Perturbed Order-Flow Observation

Computer Science and Game Theory 2026-05-28 v4 Cryptography and Security Probability Trading and Market Microstructure

Abstract

Privacy-preserving cryptocurrency exchanges (shielded AMMs, batched swap auctions, sealed-bid order-flow auctions) alter what the pricing mechanism observes about order flow. We derive the unique linear Kyle equilibrium when a committed Bayesian market maker observes order flow perturbed by independent Gaussian privacy noise. The price-impact coefficient and informed-trader strategy both rescale by a single factor in the privacy parameter, and their product is invariant. A welfare decomposition then identifies a closed-form per-period transfer from the protocol's LP pool to traders -- the "privacy subsidy", the break-even fee any privacy-aggregated exchange must charge. The result is the single-period closed-form privacy-noise analog of Loss-Versus-Rebalancing (Milionis et al. 2022). The primary application is shielded AMMs with explicit additive-noise injection (e.g., differential privacy); related designs (batched swaps, sealed-bid auctions, oracle-pegged crossings) require separate frameworks that we leave to future work.

Keywords

Cite

@article{arxiv.2605.15746,
  title  = {The Privacy Subsidy: Kyle's $\lambda$ under Noise-Perturbed Order-Flow Observation},
  author = {Yuki Nakamura},
  journal= {arXiv preprint arXiv:2605.15746},
  year   = {2026}
}

Comments

v4: Bib fix. 16 pages, 1 figure