ZKP7.2 Polynomial Commitments Based on Error-correcting Code

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ZKP学习笔记

ZK-Learning MOOC课程笔记

Lecture 7: Polynomial Commitments Based on Error-correcting Codes (Yupeng Zhang)

7.2 Polyneomial commitment based on error-correcting codes

• Recall: polynomial commitment

  • Four algorithms
    • Keygen, Commit, Eval, Verify
      

• Polynomial coefficients in a matrix
  

  • The coefficients of the polynomial is a matrix

  • If the number of degrees of the polynomial is not the power of an integer, you can pad it.
    

  • Argument for Vec-Mat product

    • Polynomial commitment with $\sqrt{d}$ proof size

• Encoding the polynomial

  • Design a scheme to test the Vec-Mat product without sending the matrix directly to the verifier
    

    • Encode each row of the matrix to a linear code

• Committing the polynomial: Merkle Hash Tree

  • Commit to each column of the encoded matrix using Merkle tree (each column is a leaf, verifier can choose a column to ask prover to open)

• Step 1: Proximity test
  

  • Ligero [AHIV’2017] and [BCGGHJ’2017]
    • [BCGGHJ’2017] : Ideal linear commitment model. Linear-time encodable code → first SNARK with linear prover time
  • One optimization
    • Prover decodes the proof to a message (size k), and sends k to the verifier
      • Decrease the size of proof from n to k
      • The Verifier does not take the first step of check

• Step 2: Consistency check

  • The vector u is not random chosen, so the Proximity test is necessary.
    

• Summary: Poly-commit based on linear code

  • Keygen: sample a hash function
  • Commit: encode the coefficient matrix of f row-wise with a linear code, compute the Merkle tree commitment
  • Eval and Verify:
    • Proximity test: random linear combination of all rows, check its consistency with t random columns
    • Consistency test: $u\times F=m$, encode m and check its consistency with t random columns
    • f(u) = $m\times u^{\prime }$

• Properties of the polynomial commitment

  • Keygen: O(1), transparent setup!
  • Commit:
    • Encoding: O(d logd) field multiplications using RS code, O(d) using linear-time encodable code
    • Merkle tree: O(d) hashes, O(1) commitment size
  • Eval: O(d) field multiplications (non-interactive via Fiat Shamir)
  • Proof size: O($\sqrt{d}$)
  • Verifier time: O($\sqrt{d}$)

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