zkLinked ✨ — Privacy-First Professional Verification

The Problem: LinkedIn, AngelList, and other platforms force you to reveal sensitive career data to prove credibility. This creates privacy risks, discrimination, and fake profiles.

Our Solution: Prove your professional credentials with cryptographic certainty — without exposing private details like employer names, exact salaries, or personal endorsements. 🛡️✨

How ZK Proofs Work Here 🔐

1. Experience Verification: Circuit validates you worked X years without revealing where
2. Education Proof: Confirms your degree is from a verified institution (hash-based)
3. Salary Range Check: Proves your compensation falls within a range (exact amount stays private)
4. Skill Endorsements: Weighted validation from verified professionals (endorser identities protected)
5. Certification Status: Active, unexpired credentials from trusted registries

Real-World Use Case: María, Blockchain Consultant 💼

María's Situation:

• 5 years of blockchain consulting experience
• Studied at MIT (wants to keep private for security)
• Has active AWS certification
• Wants to apply for a $150k premium project requiring verification

What María Does:

1. Installs zkLinked app and completes verification form
2. Uploads documents (CV, diplomas, certifications) - app generates hashes automatically
3. Generates ZK proof using our circuits
4. Shares proof + public outputs (keeps raw data private)
5. Employer verifies the proof cryptographically
6. Result: María accesses the $150k project without revealing she worked at Google, earned exactly $85k, or studied at MIT

Why Hide University Information? 🎓

Real scenarios where hiding university is valuable:

1. Socioeconomic Discrimination 💰

• Problem: "We only hire from Ivy League"
• ZK Solution: Prove quality education without revealing if it was Harvard or public university
• Result: Opportunities based on competence, not prestige

2. Geographic Prejudice 🌍

• Problem: "We don't hire from X country/region universities"
• ZK Solution: Verify education without revealing nationality/location
• Result: Global talent without geographic bias

3. Identity Protection 🛡️

• Problem: Stalkers, harassers can track by university
• ZK Solution: Verify credentials without exposing personal information
• Result: Personal security + professional credibility

4. Unfair Competition ⚔️

• Problem: Competitors can "steal" talent based on university
• ZK Solution: Headhunters can't easily identify candidates
• Result: Protection of valuable talent

5. Market Flexibility 🚀

• Problem: "We only hire from Stanford for blockchain"
• ZK Solution: Prove technical competence without university brand bias
• Result: True meritocracy based on skills

Real-World Use Cases 🌍

Freelancers & Consultants 💼

• Prove expertise without exposing client names
• Access premium projects requiring verified credentials
• Command higher rates through cryptographic credibility

Job Seekers 🎯

• Stand out in applicant pools with verified profiles
• Prevent discrimination based on employer history
• Build reputation portably across platforms

Employers & Recruiters 🏢

• Filter candidates automatically with verified credentials
• Reduce time wasted on fake profiles and inflated resumes
• Ensure compliance with hiring regulations

Networking & Mentorship 🤝

• Connect with real professionals (anti-bot verification)
• Find mentors based on verified expertise, not marketing
• Build relationships on cryptographic trust

Anti-Fraud Mechanisms 🛡️

• Cryptographic Integrity: Impossible to forge ZK proofs
• Sybil Resistance: Requires multiple endorsements from different verified users
• Registry Validation: Institutions/certifications must be in official databases
• Temporal Decay: Older endorsements have reduced weight
• Cross-Validation: Multiple verification sources prevent single points of failure

What’s inside 📦

• circuits/ — the ZK factory (Noir circuits)
  • src/main.nr — orchestrates all checks
  • src/experience.nr — work experience logic
  • src/education.nr — education validity
  • src/certifications.nr — active certifications
  • src/skills.nr — endorsements-based skills
  • src/reputation.nr — final aggregate score
  • src/utils.nr — helpers (Poseidon2 hash, Merkle, etc.)
  • Prover.toml — ready-to-run example inputs
  • Nargo.toml — Noir project configuration

How it works (like I’m five) 🧸

1. You give the circuit your private data (years worked, school hash, skills).
2. The circuit checks everything secretly.
3. It only reveals tiny truths: “meets min years?”, “education OK?”, “skills OK?”, and a final score.
4. Anyone can verify you’re honest — without seeing your private info.

User flow 👩‍💻➡️🔐➡️✅

1. Edit circuits/Prover.toml (we’ve included a complete example).
2. Run the commands to build a proof.
3. Share the proof + public outputs (keep raw data private).

Run it 🚀

cd circuits
nargo check       # compile the circuit
nargo execute     # generate the witness from Prover.toml
# generate the ZK proof (Barretenberg's bb CLI tool)
bb prove -b ./target/circuits.json -w ./target/circuits.gz -o ./target   
# verify the proof using public outputs
bb write_vk -b ./target/circuits.json -o ./target
bb verify -k ./target/vk -p ./target/proof -i ./target/public_inputs
# verifier solidity contract
bb write_solidity_verifier -k ./target/vk -o ./target/Verifier.sol

Note: If you edit Prover.toml, keep array sizes exact (e.g., 100 institutions, 200 certs).

What we validate today ✅

• Experience: years computed from start_years/end_years + sanity checks (no future dates, etc.).
• Salary: “is current salary within [min, max]?” (it doesn’t reveal the exact salary).
• Education: the institution_hash must be present in a provided whitelist.
• Certifications: each must exist in a registry and be unexpired.
• Skills: endorsements are weighted and averaged; requires a minimum count.
• Reputation: a weighted sum of the above into a final score.

Privacy & crypto bits 🛡️

• Poseidon2 hashing for ZK-friendly commitments & nullifiers (in utils.nr).
• Merkle root helper available to move large lists to membership proofs (future step).
• Only aggregated public outputs are revealed; raw inputs stay private.

Example public outputs 🧾

{
  "experience_verified": 1,
  "salary_range_verified": 1,
  "education_verified": 1,
  "certifications_verified": 1,
  "skills_verified": 1,
  "overall_reputation_score": 100
}

What stays private 🔒

• Company names, exact salaries, specific institutions (we use hashes), raw endorsements.

Roadmap & Next Steps 🔭

• Replace whitelists with Merkle roots and private membership proofs
• Emit public nullifiers (anti-replay) and integrate on-chain verification
• Smart contracts: verify proofs on-chain and mint badges based on scores
• Frontend: generate proofs client-side with Noir.js + Web Workers for UX
• Mobile app with biometric verification integration

Tech highlights ⚙️

• Noir v1 beta compatible (nargo 1.0.0-beta.11).
• Poseidon2 dependency poseidon@v0.1.1 for efficient hashing.
• Modular circuits for maintainability and cheaper constraints.

Troubleshooting 🧯

• If nargo execute fails, double-check array sizes in Prover.toml (must match exact lengths).
• If hashing libraries conflict, ensure your Nargo.toml uses poseidon = { tag = "v0.1.1", git = "https://github.com/noir-lang/poseidon" }.

Why This Matters 🌟

For Individuals: Take control of your professional data while proving credibility For Organizations: Reduce hiring risks and improve talent quality
For Society: Create a more trustworthy, privacy-preserving professional ecosystem

Deploy Address NFT

forge script script/DeployNFT.s.sol --rpc-url $RPC_URL --private-key 0x$PRIVATE_KEY --broadcast

[Success] Hash: 0x33e64a9705e70a66484b38405ea4814b860a37ee6a5427e794120e11c78c7f42 Contract Address: 0x7b28E980b9FCAD6d83cDECdBBC71e4e130a0aeF7

Ready to prove your worth — privately? 🧪✨