ruvnet--RuView/docs/research/BFLD/09-github-issue.md

GitHub Issue Draft

Title: feat: BFLD — Beamforming Feedback Layer for Detection (privacy-gated WiFi sensing)

Labels: enhancement, privacy, security, area/signal, area/firmware

Milestone: (TBD — suggest: v0.8.0)

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Summary

Add a new crate wifi-densepose-bfld that turns raw 802.11 Beamforming Feedback Information (BFI) into bounded, privacy-gated sensing outputs. BFLD detects when RF data crosses from "ambient sensing" into "identity record" and structurally prevents identity-correlated data from leaving the node.

This is the safety layer that was missing from the CSI pipeline. As passive BFI sniffing tools (Wi-BFI, PicoScenes) become widely available and academic attacks (BFId at ACM CCS 2025, LeakyBeam at NDSS 2025) demonstrate >90% re-identification from commodity WiFi, the wifi-densepose ecosystem needs an explicit privacy layer before scaling deployment.

Motivation

1. BFI is plaintext and passively sniffable. IEEE 802.11ac/ax CBFR frames are transmitted before WPA2/WPA3 encryption is applied. Any nearby device in monitor mode can capture them (NDSS 2025: https://www.ndss-symposium.org/ndss-paper/lend-me-your-beam-privacy-implications-of-plaintext-beamforming-feedback-in-wifi/).

2. BFI enables re-identification. The KIT BFId paper (ACM CCS 2025: https://dl.acm.org/doi/10.1145/3719027.3765062) demonstrates >90% identity recognition from 5 seconds of BFI, from a dataset of 197 individuals, using only the Phi/Psi Givens rotation angles.

3. The existing pipeline has no identity-leakage measurement. The rvCSI pipeline produces presence/motion/pose events without any indication of whether those outputs were derived from identity-discriminative data. An operator deploying in a care facility or shared office has no way to verify the system is behaving anonymously.

4. WiFi 7 will make this worse. 802.11be (Wi-Fi 7) multi-link operation increases sounding frequency 3–5×. The attack surface is not static.

Proposed Solution

New crate at v2/crates/wifi-densepose-bfld/ with the following pipeline:

BFI capture (CBFR frames, Pi 5 / Nexmon monitor mode)
    → BFI extractor (Phi/Psi parser, 802.11ac/ax)
    → Normalization + temporal windowing
    → Feature extraction (9 named features)
    → Identity risk engine (in-RAM embeddings, coherence gate)
    → Privacy gate (privacy_class byte, field masking)
    → MQTT emitter (per-class topic routing)

Three structural invariants (not configurable, not policy):

1. Raw BFI never leaves the node.
2. Identity embedding is in-RAM-only (VecDeque, never persisted).
3. Cross-site identity matching is cryptographically impossible via per-site BLAKE3 keyed hash with daily rotation.

Output events published on ruview/<node_id>/bfld/{presence,motion,person_count,...}/state.

Matter and HA expose only: presence, motion, person_count. Identity fields are rejected at both boundaries.

Acceptance Criteria

• AC1: Parser handles 802.11ac VHT and 802.11ax HE CBFR frames at 20/40/80/160 MHz, 2×2 through 4×4 MIMO.
• AC2: Presence detection latency ≤ 1s p95 from first non-empty BFI frame in a new occupancy event.
• AC3: Motion score published at ≥ 1 Hz on ruview/<node_id>/bfld/motion/state during sustained occupancy.
• AC4: Raw BFI bytes (Phi/Psi angle matrices) are never present in any serialized output at any privacy_class value.
• AC5: Privacy mode suppresses all identity-derived fields (identity_risk_score, rf_signature_hash, identity_embedding) from all outbound events.
• AC6: Identical BfiCapture input → bit-identical BfldFrame output (deterministic, cross-platform).
• AC7: Pipeline produces valid BfldEvent with csi_matrix = None (BFI-only mode), without panic or significant accuracy degradation.

References

• BFId paper: https://dl.acm.org/doi/10.1145/3719027.3765062
• KIT BFId dataset: https://ps.tm.kit.edu/english/bfid-dataset/index.php
• LeakyBeam (NDSS 2025): https://www.ndss-symposium.org/ndss-paper/lend-me-your-beam-privacy-implications-of-plaintext-beamforming-feedback-in-wifi/
• Wi-BFI tool: https://arxiv.org/abs/2309.04408
• Protecting activity signatures in CSI feedback: https://arxiv.org/pdf/2512.18529
• Research bundle: docs/research/BFLD/ (this repo)
• Draft ADR: docs/research/BFLD/08-adr-draft.md → ADR-118

Out of Scope

• Preventing passive BFI capture by external attackers (hardware-level problem, not software).
• Differential privacy noise injection (noted as future extension in ADR-118).
• Federated identity learning (local-only is sufficient for the current use case).
• BFI capture directly from ESP32-S3 firmware (Espressif API does not expose CBFR; host-side Pi 5 / Nexmon capture is the implementation path).
• WiFi 7 / 802.11be multi-link BFI (frame format versioning accommodates it; not in scope for v1 implementation).

Related Issues / PRs

• ADR-028 witness bundle (ref: this repo's docs/WITNESS-LOG-028.md)
• ADR-115 HA integration (21 entities — BFLD adds 6 more)
• ADR-116 Matter seed packaging (cog-ha-matter crate needs Matter boundary update)
• ADR-117 pip modernization (PyO3 pattern reused for BFLD Python bindings)
• rvCSI platform (ADR-095/096) — Nexmon adapter shared with BFLD BFI capture path