Internet 2.0 Privacy‑Respectful Capture System Proposal
A next‑generation, opt‑in privacy layer for real‑world gatherings where online communities meet offline.Attendees keep full control over their likeness while still allowing vibrant media to be shared.
1. Executive Summary
A next‑generation, opt‑in privacy layer for real‑world gatherings where online communities meet offline. The system combines a consent registry with real‑time AI image synthesis to automatically blur or avatar‑swap any attendee who has not granted capture permission. Attendees keep full control over their likeness while still allowing vibrant media to be shared.
2. Problem Statement
* Communities that originate online often value anonymity and operational security (op‑sec).
* Offline events jeopardize that anonymity because consumer devices default to unrestricted photo/video capture.
* Manual “no‑photos” policies rely on social enforcement and routinely fail.
3. Goals & Success Metrics
| Goal | Metric |
|---|---|
| Respect individual capture preferences | 100% of images containing non‑consenting faces are modified before storage |
| Preserve social‑media friendliness | ≤ 200 ms added latency per photo on common smartphones |
| Low friction for attendees | < 2 min onboarding; zero extra steps for photographers after setup |
4. User Personas
- Anon – full online anonymity; refuses any real‑world likeness capture.
- Pseud – pseudonymous persona; willing to appear if rendered as chosen avatar.
- Open – public persona; happy to appear unmodified.
5. Core Requirements
Functional
- Real‑time face detection & classification against consent registry.
- Three modification modes: Blur, Pixelate, Avatar‑swap (3‑D).
- On‑device processing with offline fallback; no raw images leave device without local user approval.
- Dynamic consent toggle; changes propagate instantly via BLE mesh.
- Audit log for dispute resolution.
Non‑Functional
- Latency ≤ 200 ms per frame (photo) / ≤ 80 ms per frame (video @30 fps, optional frame‑skipping).
- Battery impact ≤ 15 % per 8‑hour event use.
- End‑to‑end AES‑256 encryption of consent data; zero‑knowledge storage.
6. Solution Architecture Options
Option A – Pure Mobile App
- iOS & Android app doubles as both camera and consent beacon.
- Uses device GPU/NN API (Apple Core ML / Google NNAPI) for inference.
- Pros: no extra hardware; quick MVP. Cons: third‑party cameras unfiltered.
Option B – Mobile App + Wearable BLE Badge
- Attendees wear small BLE “persona badges” broadcasting a hashed ID and capture flag.
- Any nearby camera app with SDK decodes signal to apply rules.
- Pros: works with DSLRs/dedicated cams via hot‑shoe accessory; clear visual cue.
Option C – Dedicated Event Cameras
- Organizers deploy provided cameras at key spots (stage, photobooth).
- Cameras run onboard NPU + consent registry; smartphones remain unmanaged.
- Pros: highest predictability. Cons: limited angles; highest cost.
7. AI Processing Pipeline
- Detection – MTCNN or BlazeFace (mobile) locates faces.
- Identity Hashing – Lightweight face embedding (FaceNet‑lite) → 128‑D vector.
- Consent Lookup – Compare vector to local encrypted DB (cosine < 0.4 → match).
- Render Engine
Blur()– Gaussian radius auto‑scaled.AvatarSwap()– Tri‑mesh morph to pre‑generated 3‑D model (GLB) using Live2D rig.
- Post‑Processing – Tone mapping, export JPEG/MP4.
8. Consent & Identity Management
- Each attendee sets preference in app during ticket check‑in.
- Generates anonymized face embedding stored only on personal device & optional backup to event DB (encrypted, event‑limited TTL).
- BLE mesh syncs preference bitmask every 5 s.
- Emergency “Panic” toggle instantly flips to Blur‑All.
9. Hardware Spec (Badge Prototype)
| Component | Spec | Est. Cost |
|---|---|---|
| MCU | Nordic nRF5340 BLE SoC | $4.20 |
| Battery | 200 mAh Li‑Po (24 h) | $1.00 |
| LED | RGB status (capture allowed/blocked) | $0.05 |
| Case | 3‑D printed bioplastic | $0.60 |
| Total | $5.85 |
10. Privacy & Security
- All face embeddings are non‑invertible.
- Open‑source cryptographic libraries; third‑party audits before launch.
- Optional zero‑knowledge proofs to verify consent status without revealing identity.
11. Implementation Roadmap
| Phase | Timeline | Milestones |
|---|---|---|
| 0 – Feasibility R&D | Aug–Sep 2025 | POC on iPhone 15 & Pixel 9; latency benchmark |
| 1 – MVP App | Oct–Dec 2025 | Beta at 30‑person meetup; collect feedback |
| 2 – Badge Pilot | Jan–Mar 2026 | Produce 200 badges; integrate SDK into DSLR bridge |
| 3 – Full Launch | Q2 2026 | Open‑source release; manufacturing partner secured |
12. Risks & Mitigations
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| Face recognition bias | Med | High | Diverse training set + on‑device calibration |
| Latency on older devices | High | Med | Fallback to still‑photo‑only mode |
| User adoption friction | Med | Med | Gamified onboarding; visual badge cues |
13. Future Extensions
- AR visor overlay for real‑time avatar rendering in live view.
- Decentralized ID verifiable credentials (DID) for cross‑event consent.
- Integration with privacy‑first social networks; auto‑tagging with persona handles.
14. Open Questions
- Minimum hardware compatibility matrix?
- How to handle group shots with mixed consent states elegantly?
- Acceptable fallback when no consent data received (e.g., distant photographers)?
15. Enforcement & Community Governance
By making compliance friction‑free, the system removes all plausible deniability. Any attendee who circumvents or disables the consent layer is demonstrably acting in bad faith and can be confidently barred from future events.