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

topic: current_projecttop score: 98released: 2026-05-18first surfaced: 2026-05-18arXivPDFlinked_to_results2026-05-18

Authors: Yuki Nakamura

arXiv · PDF

Summary

arXiv:2605. 15746v1 Announce Type: cross Abstract: Privacy-preserving cryptocurrency exchanges (shielded AMMs, batched swap auctions, sealed-bid order-flow auctions) alter what the pricing mechanism observes about order flow.

Relevance

Read next because The Privacy Subsidy: Kyle's $\lambda$ under Noise-Perturbed Order-Flow Observation overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)". Matching terms: under, line, rate, factor, position. Source: arxiv cs.CR (Cryptography and Security).

Abstract

arXiv:2605.15746v1 Announce Type: cross 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.