Chapter 2: The Power of Randomness (Fingerprinting and Freivalds’ Algorithm)¶
Reed-Solomon Fingerprinting¶
Explore more details in the Reed-Solomon Fingerprinting documentation.
Freivalds’ Algorithm f'rei volz¶
Freivalds’ Algorithm involves verifying the equality of matrices \(C\) and \(D\) received from the prover.
The Setting¶
| Symbol | Definition | Note |
|---|---|---|
| p | modulo | \(p\geq\max(m, n^2)\) |
| \(\mathbb{F}_p\) | finite field over p | |
| r | random value in \(\mathbb{F}_p\) | |
| x | \(x = (1,r,r^2,...,r^{n-1})\) | |
| n | number of rows/cols in a matrix | |
| \(C\) | prover’s result matrix | |
| \(D\) | the real result matrix |
Steps¶
- Calculate \(y = Cx\) (see example).
- Calculate \(z = A.Bx\).
- Compare \(y == z\)?
Runtime¶
This approach shortens the comparison time:
- A.B (\(n^{2.37}\))
- Cx (\(n^2\)), A.Bx (\(2n^2\)), y == z (\(2n\)) => Total runtime: \(n^2\)
Reason: The time complexity of multiplying a (n x n) matrix and a vector of size n is \(O(n^2)\).
Examples¶
- Multiply a matrix by a vector
Consider matrices:
The product \(C\) is obtained as follows:
\(C_{1,1} = 3r^2 + 2r + 1\)
\(C_{2,1} = 6r^2 + 5r + 4\)
\(C_{3,1} = 9r^2 + 8r + 7\)
Resulting in:
An Alternative View of Fingerprinting and Freivalds’ Algorithm¶
Suppose we want to compare two vectors, \(a\) and \(b\). We first convert them into two polynomials over the field \(\mathbb{F}_p\), where \(p\) is much larger than \(n\). As can be seen from the image below, if we evaluate these two polynomials at any points, the results are likely to differ from each other.
Univariate Lagrange Interpolation¶
There are other ways to interpret \(a\) as the description of a univariate polynomial \(q_a\) of degree \(n−1\).
Lemma 2.3: For any vector \(a = (a_1,...,a_n)\) ∈ \(F_n\), there is a unique univariate polynomial \(q_a\) of degree at most \(n−1\) such that: \(q_a(i) = a_{i + 1}\) for i: \(0 \rightarrow n - 1\)
Now, use Lagrange basis polynomials to find \(q_a\) \(δ_i(X) = \prod_{k: 0-> n - 1, k != i} (X - k) / (i - k)\)
Then: \(q_a(X) = \Sigma_{i = 0}^{n - 1} a_{j + 1} * δ_i(X)\)
\(q_a\) is often called the univariate low-degree extension (LDE) of \(a\)
Runtime¶
O(n) additions, multiplications, and inversions over \(F_p\). The reason is that \(δ_i(r)\) can be computed from \(δ_{i - 1}(r)\):
\(δ_i(r) = δ_{i−1}(r)·(r −(i−1))·(r −i)^{-1}·i^{-1}·(−(n−i))\)