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Claude 006a66ca20 Hyper-optimize VEIL shield: derive the optimal config instead of hand-picking it

Adds an `optimize` module that replaces the hand-picked shield config with a
derived, robustness-verified optimum, and hardens the experiment so the
collapse is proven to be signal-level, not classifier-level.

Model changes:
- throughput.rs: add a feedback-airtime term (cost rises with feedback bits)
  alongside the falling quantization residual, giving a genuine interior
  throughput optimum in feedback resolution.
- attacker.rs: add a selectable distance metric (Euclidean + Cosine) so the
  optimizer can require the collapse to hold under multiple classifiers.
- experiment.rs: thread the attacker metric through; build the channel once.

optimize.rs:
- optimal_feedback_bits / spec_optimal_feedback_bits: throughput-best resolution
  (3 bits unconstrained, matching DySPAN-2026; 5 bits within the 802.11 {5,7,9}
  set).
- min_givens_passes: smallest mixing budget that collapses re-ID robustly across
  both metrics AND N in {16,32}.
- pareto_frontier and hyper_optimize.

Findings and adopted defaults:
- Proven-minimum robust passes = 48; the hand-picked 112 was 2.3x over-
  provisioned. Rotation mixing is keyed (never signaled), so extra passes are
  throughput-free -> ship 96 (2x margin).
- Feedback resolution 5 bits (spec-optimal), down from 7.
- ShieldConfig::default() now equals hyper_optimize()'s output; a test guards
  against drift.

Net vs. the original: strictly better on BOTH privacy and throughput.
Reference (SYNTHETIC/L0, N=16): re-ID 100% shield-off -> 4.7% shield-on
(chance 6.25%, below chance), throughput 97.6%, energy ratio 1.000000. 35 tests
+ doctest pass; clippy -D warnings clean; builds for wasm32.

Docs: new docs/research/privacy-shield/08-optimization.md; updated bundle
README/03/05/07 and ADR-288 with the derived operating point.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01WEXNqzs7UsfNFBcP5yW21p

2026-08-09 14:08:46 +00:00

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07 — Implementation and Roadmap

1. What ships in this bundle

Reference crate v2/crates/wifi-densepose-privshield (VEIL): a deterministic, dependency-free, WASM-ready pure-compute leaf implementing the full attacker-vs-protector experiment, the four compliant controls, the throughput model, the compliance audit, the optimize hyper-optimizer, and a byte-stable proof. 35 tests + doctest pass; builds for wasm32-unknown-unknown; clippy-clean.
This research bundle (docs/research/privacy-shield/).
ADR-288 — the formal decision record.

The crate is intentionally a leaf with no internal RuView dependencies (mirrors wifi-densepose-aether), so it can be reasoned about, fuzzed, and ported independently, and so it can never accidentally acquire a path to a radio.

2. Reuse map (how VEIL composes with existing RuView)

Existing subsystem	Relationship
BFLD (ADR-118/120/121, `wifi-densepose-bfld`)	Detection layer. Its `identity_risk_score` is the natural trigger for VEIL's `SensingDetector` — detect leakage, then shield
Privacy control plane (ADR-141)	VEIL protection steps emit `ComplianceReport`s that fit the runtime-attestation model (which mode, which actions, which fields)
Active sensing / governed actuation (ADR-280)	VEIL is a defensive `SensingAction`: a governed, privacy-ceiling-bounded emission-shaping action the control plane can schedule
Givens/beamforming primitives	VEIL reuses the report's native Givens-rotation structure rather than inventing a new transform
Deterministic proof discipline (`nvsim`, `archive/v1/verify.py`)	VEIL's `proof` module follows the same pinned-witness pattern

3. Phased rollout

Phase	Deliverable	Evidence class
P1 — reference model (this PR)	Crate + experiment + docs + ADR	SYNTHETIC (cargo test)
P2 — sensitivity study	Sweep N, noise, resolution, mixing; add a learned attacker to confirm signal-level collapse	SYNTHETIC
P3 — BFLD integration	Wire `identity_risk` → `SensingDetector` → shield engage; emit attestation	SYNTHETIC + integration tests
P4 — firmware feedback shaping	Implement keyed fine-subspace rotation + cadence randomization in ESP32/Nexmon feedback path	build + hardware
P5 — two-node hardware measurement	Wi-BFI attacker vs. VEIL protector on real silicon; iperf throughput; captured log	MEASURED (with witness)
P6 — deployment profiles	Per-segment profiles (SCIF, boardroom, ward) with regulatory review	operational

No defense claim graduates from SYNTHETIC to MEASURED without a captured boot/runtime log (CLAUDE.md hardware rule).

4. Open problems (tracked honestly)

Real-hardware separability. Comm and identity information are only approximately separable on real radios; the true throughput cost of full identity hiding may exceed the model's ~2%. P2/P5 must bound it.
Within-session motion leakage. A fixed per-session rotation does not obfuscate coarse motion within one capture window. Needs stronger cadence randomization or amplitude shaping; currently a stated non-goal for the re-ID metric.
Active adversary (A2). An attacker that transmits its own soundings is only partially addressed by cadence control; a MAC-layer non-response policy is needed.
Key management. The per-session rotation key must be derived from the negotiated link secret; VEIL's PRNG is explicitly not cryptographic and must not be used for real key material.
Regulatory review per jurisdiction. The energy-conservation argument is portable, but power/mask/timing limits and any transmit-nulling profile need local review before field use.

5. Validation commands

# Reference experiment + all unit/proof/doc tests
cargo test -p wifi-densepose-privshield --no-default-features

# WASM portability (leaf builds with no radio path)
cargo build -p wifi-densepose-privshield --target wasm32-unknown-unknown

# Lints
cargo clippy -p wifi-densepose-privshield --all-targets

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