Verifiable Randomness with Context: Understanding VRF with Additional Data (VRF-AD)

Imagine you're part of a decentralized lottery. A cryptographic function decides if you're the winner. You prove your eligibility with a random-looking output that only you could have generated. This is the power of a VRF — and today, we’ll explore how adding context through additional data (AD) makes it even more secure and versatile.

In this blog we’ll cover:

• What is a Verifiable Random Function (VRF)?
• What is Additional Data (AD)?
• Why does AD matter in modern cryptographic protocols?
• How does VRF-AD work?
• Real-world use cases and examples

What is a Verifiable Random Function (VRF)?#

A Verifiable Random Function (VRF) is like a cryptographic hash function, but with a twist: it's keyed. Only someone with a secret key can generate a value, but anyone with the public key can verify it was done correctly.

Key Properties:#

• Deterministic: Same input always yields the same output
• Pseudorandom: Output looks random to everyone else
• Verifiable: Comes with a proof that anyone can check

Use Cases:#

• Leader election in blockchains (e.g., Algorand)
• DNSSEC (NSEC5)
• Randomness in smart contracts (e.g., Chainlink VRF)

While VRFs are powerful, they have limitations:

1. Replay#

Without binding to context, someone could reuse a valid output + proof in a different domain or application.

2. Lack of Domain Separation#

The same input used in two different systems could produce the same output and proof, potentially leaking data or leading to misuse.

3. Insecure in Privacy-Preserving Systems#

Protocols like zero-knowledge proofs or ring VRFs need stronger proof binding to ensure anonymity and contextual correctness.

VRF-AD enhances the classic VRF by allowing users to include contextual metadata — called “additional data” or AD — into the verifiable proof.

What's Different?#

• The VRF output remains the same
• The proof is now bound to both the input and the additional data

This ensures the proof is only valid within the given context.

Context Binding#

Proofs are now tightly linked to the context they were created for (e.g., "election-2025").

Replay Protection#

Even if someone captures a valid output and proof, they cannot reuse it in another domain or application.

Domain Separation#

Same input string yields unique proofs in different applications.

Enhanced Privacy#

Essential for protocols involving anonymity, such as ring signatures and zero-knowledge VRFs.

Let’s walk through the simplified process:

1. Input Conversion#

I = hash_to_curve(input)

This maps the input to a point on the elliptic curve.

2. Output Generation#

O = sk * I

This scalar multiplication produces the VRF output.

3. Proof Construction#

The proof π is generated using the input and the additional data:

π = Prove(sk, input, ad)

4. Verification#

Verify(pk, input, ad, output, π)

The verifier checks the proof is valid for both the input and the context.

JAM (Join- Accumulate Machine)#

In JAM, VRF-AD technology will be crucial for:

• Ensuring fair participation in decentralized consensus
• Verifiable randomness for selection processes
• Preventing manipulation through contextual binding
• Enhancing privacy in user interactions

ZK Voting Systems#

Privacy-preserving voting protocols use Pedersen or Ring VRFs with AD to ensure anonymous yet verifiable participation.

As cryptographic functions become foundational across layers of digital infrastructure — from randomness to identity and privacy — adding context is no longer optional. VRF-AD offers a simple yet powerful upgrade that makes VRFs safer, more flexible, and future-proof.