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R5 — Privacy and research ethics

Status: Wave 1 research recommendation
Repos: tunes (privacy model + consent contracts); implemented in tunes-ios (local control) and tunes-web (upload, retention, withdrawal)
Must-read inputs: project-charter.md, scope-statement.md, 01-assumptions-and-open-questions.md

Current disposition: ADR-004/005 accept the raw-on-device and derived-only defaults; ADR-013 names the pre-incorporation controller. Exact retention and lawful-basis wording still require counsel review.


1. Decision questions

  1. Should raw audio ever leave the device?
  2. Should the default contribution be derived features only?
  3. What GDPR / ethics posture fits a London-first open programme that is not TfL-operated or endorsed?

Working assumptions this doc reinforces:

  • Privacy default = local record → review → consent → derived-only upload
  • Prefer derived over raw unless raw is demonstrably necessary
  • Offline-first during collection

2. Local-first 8-step contribution flow (required)

Every scientific contribution path in tunes-ios should implement this sequence. Nothing leaves the device before step 7 completes successfully.

StepActionPrivacy purpose
1Record locallyCapture under user control; offline-capable
2Process locally where practicalDerive metrics/spectra without requiring upload
3Infer journey sectionsAttach energy to station-to-station units without streaming audio
4User review / correctFix alignment; prevent wrong-place disclosure
5Trim or deleteRemove unwanted time ranges; discard session
6Show exact upload previewInformed choice; no hidden fields
7Explicit consentPurpose, tier, retention, withdrawal path stated
8Upload only selected informationLeast data necessary for the chosen tier

Product rules

  • No silent background upload of audio or metrics.
  • Default selected payload = derived acoustic features + consented metadata.
  • Raw PCM remains on device unless the user opts into a separate, clearly labelled tier (if offered at all).
  • After successful derived upload, offer prominent delete local raw (and make deletion real, not soft-hide).
  • Preview must list field categories the user can understand (levels, bands, section IDs, device model class, flags) — not only a legal wall of text.

3. Why the derived-only default is the ethical centre of gravity

Railway carriages are shared spaces. Continuous microphone capture will inevitably include incidental speech (passengers, staff, PA). Derived features (e.g. LAeq,T, LCeq, LCpeak, band Leq, clipping flags) can support the programme’s public map and open methodology goals with far less speech reconstructability than waveform upload.

Prefer derived because it:

  • Reduces personal-data intensity of the default path
  • Shrinks breach blast radius (storage, backups, research exports)
  • Aligns with “useful to professionals / honest to passengers” without building a speech corpus
  • Matches the charter’s privacy-preserving and auditable principles

Raw audio remains scientifically useful for some validation (algorithm audits, disputed events, research replication). That usefulness does not justify making raw the default.


4. GDPR-oriented data tiers (working model)

ADR-013 identifies the pre-incorporation data controller; the legal pack must be updated when the CLG becomes controller. The technical tiers below assume UK GDPR / DPA 2018 duties bind the controller operating tunes-web uploads and tunes releases.

Tier framing (contribution / retention, not acoustic quality A–E)

Privacy tierPayloadTypical lawful-basis sketchRetention postureWho may access
P0 — Local onlyRaw + derived stay on deviceN/A (no transfer)User-controlledUser
P1 — Derived openMetrics, spectra summaries, section links, quality flags, coarse device classConsent for research/open publication (confirm with counsel)Versioned public releases with minimisationPublic / researchers
P2 — Derived restrictedP1 + contributor pseudonym / campaign IDs needed for withdrawal & fraud controlsConsent + documented research purposesLimited; not in bulk open dump by defaultOperators of TUNES systems; approved analysts
P3 — Short excerptsBrief waveform snippets around consented eventsSeparate explicit consent; high barShort TTL; not default open dataRestricted research
P4 — Full rawFull session audioSeparate explicit consent; rarely justifiedStrict quarantine; deletion SLAsResearch-restricted only
P5 — Research archiveAny of the above under a written protocol + ethics review where requiredConsent + ethics constraintsProtocol-defined; no silent promotion to P1Named projects only

Acoustic quality tiers A–E (R4) are orthogonal: a Tier A acoustic measurement can still be P1 (derived-only). Never conflate “scientifically strong” with “may publish raw audio.”


5. Separate recommendations by payload class

5.1 Derived-only (default — recommend ship)

Recommendation: Make this the only path in early pilots and the default forever.

  • Compute on device when practical; allow server reprocessing of already-derived uploads with versioned pipelines.
  • Publish open data from P1 fields after validation and provenance checks.
  • Include processing-pipeline version, calibration_state, quality flags, and section IDs.
  • Exclude free-text that re-identifies (or gate free-text behind stronger review — see §8).

When insufficient: algorithm disputes that require waveform inspection; rare tonal-event forensics — handle via optional higher tiers, not by weakening the default.

5.2 Short audio excerpts (optional, high friction)

Recommendation: Do not offer at public launch. Revisit only if experiments show derived features cannot answer a documented research question.

If ever offered:

  • Hard duration caps and user-visible waveform/privacy preview
  • Separate consent screen explaining speech risk
  • Default exclude from open releases (P3 → not P1)
  • Prefer event-centred clips (e.g. flagged squeal window) over continuous carriage audio

5.3 Full raw audio (discouraged as product feature)

Recommendation: Default no — raw does not leave the device, as accepted by ADR-004 (“only under restricted research protocols, if ever”).

If a future research campaign requires raw:

  • Out-of-band enrolment, not a casual in-app toggle
  • Written information sheet, purpose limitation, deletion date
  • Encrypted transit and at-rest quarantine in tunes-web
  • No use for model training that redistributes speech content
  • No implied TfL custody or endorsement

5.4 Research-restricted datasets

Recommendation: Support a governed path (P2/P5) for universities or accessibility researchers that need richer linkage (e.g. repeated contributor pseudonyms, detailed device serials for calibration studies) without placing that richness in the public dump.

  • Access agreements, purpose binding, audit logs
  • Prefer derived + metadata enrichment over raw
  • Ethics review before campaigns targeting vulnerable groups or collecting raw

6. Incidental speech

Assume speech is present in any continuous carriage recording.

ControlRequirement
Default payloadDerived features that are not intended for speech playback
User messagingState clearly that recordings may include others’ voices; derived upload is the privacy-preserving path
Open dataDo not release raw or excerpts by default
Quality flagsOptional “high speech / PA dense” flags for analysis — without uploading audio
Announcement recognitionIf ever used for alignment (R7), prefer on-device, non-retained detection; do not build a public speech archive
Staff / passenger complaintsPublish a contact + deletion path; treat seriously

TUNES is not a surveillance product. Feature design that rewards capturing intelligible conversation is out of scope.


7. Withdrawal, retention, and identifiers

Withdrawal

  • Every uploaded contribution needs a withdrawal handle the user can exercise later (contributor account, one-time recovery code, or equivalent — exact UX TBD).
  • Withdrawal must remove or tombstone personal data from operational stores and schedule exclusion from future public releases.
  • Already-downloaded third-party copies of open releases cannot be erased from the internet; the privacy notice must say so honestly.
  • Pseudonymous public rows should avoid stable cross-release personal identifiers where possible.

Retention (working policy; confirm legally)

ArtefactWorking retention
Local raw on phoneUser-controlled; encourage delete-after-upload
Derived uploads (P1/P2)Retained for dataset integrity; subject to withdrawal
Short excerpts / raw (P3/P4)Short, protocol-defined TTL; default delete
Server logsNo raw audio; redact tokens; limited TTL
Public releasesVersioned; withdrawal appears as omission in subsequent versions

Location minimisation

Underground GPS is unreliable; prefer topology snap to sections over storing precise raw tracks in open data (aligns with methodology GPS caveat). If coarse timing or station sequence is enough, do not keep high-resolution location trails in P1.


8. Children, vulnerable participants, and free text

Children

  • Do not target under-13 contributions.
  • Age-appropriate design: if a journey is planned “for a child,” that is subjective metadata about suitability, not enrolment of the child as data subject.
  • Any future youth programmes require separate safeguarding + parental consent design and legal review.

Vulnerable / sensory-sensitive contributors

  • Accessibility and quieter-journey goals are central to public value — participation must remain voluntary, with clear ability to abort, delete, and skip subjective questions.
  • Avoid pressure mechanics that encourage unsafe recording behaviour.
  • Perception items about pain, fatigue, or child suitability are sensitive; keep them optional and separate from objective metrics (R9).

Free-text reports

Free text can contain names, medical detail, or incident narratives. Recommendation: keep free text out of P1 open releases by default; moderate before any publication; prefer structured perception scales.


  • The controller and current contact stated in the legal pack
  • What is uploaded for the selected privacy tier
  • That TUNES is independent and not TfL-endorsed
  • Survey-grade limits of crowd acoustics (no false health certainty)
  • Retention, withdrawal, and open-licence implications for P1
  • Incidental speech risk

Ethics review

  • Ordinary derived-only adult pilot: document an ethics self-assessment; seek formal review if a partner university requires it or if raw/excerpt collection begins.
  • Raw audio, children, or clinical framing (“risk scores,” diagnosis language): ethics + legal before collection.
  • Claim language for exposure / discomfort / loudness / risk remains gated by R2 and a claim-language ADR — privacy docs must not invent medical authority.

10. Threat-model sketch (engineering checklist)

ThreatMitigation
Upload before consentHard client gate; server rejects unauthenticated/unconsented payloads
Raw included “by accident”Schema allow-lists; integration tests; preview shows MIME/size class
Trimmed audio reconstructedDo not upload pre-trim buffers; verify deletion
Re-identification via trajectorySection-level publication; minimise precise GPS in open data
Log leakageBan audio/text from logs; structured metrics only
Insider misuse of raw quarantineAccess control, audit, short TTL, separate storage
Malicious “research” scrape of speechDon’t publish speech-bearing tiers openly

Implement and test in tunes-ios + tunes-web per the privacy test list in research-plan.md.


11. Repo split

ConcernOwner
Privacy principles, tier definitions, public notices outline, ADRstunes
8-step flow, on-device processing, preview, local deletetunes-ios
Consent records, encryption, retention jobs, withdrawal API, release scrubbingtunes-web

Recommendation

  1. Ship the 8-step local-first flow as a non-negotiable product requirement.
  2. Default forever to derived-only (P1) uploads after preview + explicit consent.
  3. Answer “should raw leave the device?” with no by default; allow only research-restricted protocols (P4/P5) after legal/ethics gates — not as a casual consumer toggle.
  4. Do not offer short excerpts at launch; revisit only with a documented scientific need.
  5. Implement withdrawal, location minimisation, and honest limits on open-data erasure.
  6. Protect children and vulnerable users with non-targeting defaults, optional perception items, and ethics escalation for raw/clinical framing.
  7. Keep the data-controller identity and CLG transition current under ADR-013; obtain legal review before public beta.

Confidence: High for the derived-only default, 8-step flow, and raw-as-exception; Medium for exact lawful bases and retention periods until counsel reviews; Low for any raw/excerpt productisation until ethics + threat model sign-off.

Depends on experiment/legal/user-test? Yes — legal (controller, lawful basis, licences interplay); user-test (consent/preview comprehension, withdrawal UX); ethics review before raw/vulnerable campaigns; privacy threat-model exercise in Phase 4.

Links to related docs: