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Negacyclic Polynomial Multiplication

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In this article I’ll cover three techniques to compute special types of polynomial products that show up in lattice cryptography and fully homomorphic encryption. Namely, the negacyclic polynomial product, which is the product of two polynomials in the quotient ring $\mathbb{Z}[x] / (x^N + 1)$. As a precursor to the negacyclic product, we’ll cover the simpler cyclic product.

All of the Python code written for this article is on GitHub.

The DFT and Cyclic Polynomial Multiplication

A recent program gallery piece showed how single-variable polynomial multiplication could be implemented using the Discrete Fourier Transform (DFT). This boils down to two observations:

  1. The product of two polynomials $f, g$ can be computed via the convolution of the coefficients of $f$ and $g$.
  2. The Convolution Theorem, which says that the Fourier transform of a convolution of two signals $f, g$ is the point-wise product of the Fourier transforms of the two signals. (The same holds for the DFT)

This provides a much faster polynomial product operation than one could implement using the naïve polynomial multiplication algorithm (though see the last section for an implementation anyway). The DFT can be used to speed up large integer multiplication as well.

A caveat with normal polynomial multiplication is that one needs to pad the input coefficient lists with enough zeros so that the convolution doesn’t “wrap around.” That padding results in the output having length at least as large as the sum of the degrees of $f$ and $g$ (see the program gallery piece for more details).

If you don’t pad the polynomials, instead you get what’s called a cyclic polynomial product. More concretely, if the two input polynomials $f, g$ are represented by coefficient lists $(f_0, f_1, \dots, f_{N-1}), (g_0, g_1, \dots, g_{N-1})$ of length $N$ (implying the inputs are degree at most $N-1$, i.e., the lists may end in a tail of zeros), then the Fourier Transform technique computes

\[ f(x) \cdot g(x) \mod (x^N – 1) \]

This modulus is in the sense of a quotient ring $\mathbb{Z}[x] / (x^N – 1)$, where $(x^N – 1)$ denotes the ring ideal generated by $x^N-1$, i.e., all polynomials that are evenly divisible by $x^N – 1$. A particularly important interpretation of this quotient ring is achieved by interpreting the ideal generator $x^N – 1$ as an equation $x^N – 1 = 0$, also known as $x^N = 1$. To get the canonical ring element corresponding to any polynomial $h(x) \in \mathbb{Z}[x]$, you “set” $x^N = 1$ and reduce the polynomial until there are no more terms with degree bigger than $N-1$. For example, if $N=5$ then $x^{10} + x^6 – x^4 + x + 2 = -x^4 + 2x + 3$ (the $x^{10}$ becomes 1, and $x^6 = x$).

To prove the DFT product computes a product in this particular ring, note how the convolution theorem produces the following formula, where $\textup{fprod}(f, g)$ denotes the process of taking the Fourier transform of the two coefficient lists, multiplying them entrywise, and taking a (properly normalized) inverse FFT, and $\textup{fprod}(f, g)(j)$ is the $j$-th coefficient of the output polynomial:

\[ \textup{fprod}(f, g)(j) = \sum_{k=0}^{N-1} f_k g_{j-k \textup{ mod } N} \]

In words, the output polynomial coefficient $j$ equals the sum of all products of pairs of coefficients whose indices sum to $j$ when considered “wrapping around” $N$. Fixing $j=1$ as an example, $\textup{fprod}(f, g)(1) = f_0 g_1 + f_1g_0 + f_2 g_{N-1} + f_3 g_{N-2} + \dots$. This demonstrates the “set $x^N = 1$” interpretation above: the term $f_2 g_{N-1}$ corresponds to the product $f_2x^2 \cdot g_{N-1}x^{N-1}$, which contributes to the $x^1$ term of the polynomial product if and only if $x^{2 + N-1} = x$, if and only if $x^N = 1$.

To achieve this in code, we simply use the version of the code from the program gallery piece, but fix the size of the arrays given to numpy.fft.fft in advance. We will also, for simplicity, assume the $N$ one wishes to use is a power of 2. The resulting code is significantly simpler than the original program gallery code (we omit zero-padding to length $N$ for brevity).

import numpy
from numpy.fft import fft, ifft

def cyclic_polymul(p1, p2, N):
    """Multiply two integer polynomials modulo (x^N - 1).

    p1 and p2 are arrays of coefficients in degree-increasing order.
    """
    assert len(p1) == len(p2) == N
    product = fft(p1) * fft(p2)
    inverted = ifft(product)
    return numpy.round(numpy.real(inverted)).astype(numpy.int32)

As a side note, there’s nothing that stops this from working with polynomials that have real or complex coefficients, but so long as we use small magnitude integer coefficients and round at the end, I don’t have to worry about precision issues (hat tip to Brad Lucier for suggesting an excellent paper by Colin Percival, “Rapid multiplication modulo the sum and difference of highly composite numbers“, which covers these precision issues in detail).

Negacyclic polynomials, DFT with duplication

Now the kind of polynomial quotient ring that shows up in cryptography is critically not $\mathbb{Z}[x]/(x^N-1)$, because that ring has enough easy-to-reason-about structure that it can’t hide secrets. Instead, cryptographers use the ring $\mathbb{Z}[x]/(x^N+1)$ (the minus becomes a plus), which is believed to be more secure for cryptography—although I don’t have a great intuitive grasp on why.

The interpretation is similar here as before, except we “set” $x^N = -1$ instead of $x^N = 1$ in our reductions. Repeating the above example, if $N=5$ then $x^{10} + x^6 – x^4 + x + 2 = -x^4 + 3$ (the $x^{10}$ becomes $(-1)^2 = 1$, and $x^6 = -x$). It’s called negacyclic because as a term $x^k$ passes $k \geq N$, it cycles back to $x^0 = 1$, but with a sign flip.

The negacyclic polynomial multiplication can’t use the DFT without some special hacks. The first and simplest hack is to double the input lists with a negation. That is, starting from $f(x) \in \mathbb{Z}[x]/(x^N+1)$, we can define $f^*(x) = f(x) – x^Nf(x)$ in a different ring $\mathbb{Z}[x]/(x^{2N} – 1)$ (and similarly for $g^*$ and $g$).

Before seeing how this causes the DFT to (almost) compute a negacyclic polynomial product, some math wizardry. The ring $\mathbb{Z}[x]/(x^{2N} – 1)$ is special because it contains our negacyclic ring as a subring. Indeed, because the polynomial $x^{2N} – 1$ factors as $(x^N-1)(x^N+1)$, and because these two factors are coprime in $\mathbb{Z}[x]/(x^{2N} – 1)$, the Chinese remainder theorem (aka Sun-tzu’s theorem) generalizes to polynomial rings and says that any polynomial in $\mathbb{Z}[x]/(x^{2N} – 1)$ is uniquely determined by its remainders when divided by $(x^N-1)$ and $(x^N+1)$. Another way to say it is that the ring $\mathbb{Z}[x]/(x^{2N} – 1)$ factors as a direct product of the two rings $\mathbb{Z}[x]/(x^{N} – 1)$ and $\mathbb{Z}[x]/(x^{N} + 1)$.

Now mapping a polynomial $f(x)$ from the bigger ring $(x^{2N} – 1)$ to the smaller ring $(x^{N}+1)$ involves taking a remainder of $f(x)$ when dividing by $x^{N}+1$ (“setting” $x^N = -1$ and reducing). There are many possible preimage mappings, depending on what your goal is. In this case, we actually intentionally choose a non preimage mapping, because in general to compute a preimage requires solving a system of congruences in the larger polynomial ring. So instead we choose $f(x) \mapsto f^*(x) = f(x) – x^Nf(x) = -f(x)(x^N – 1)$, which maps back down to $2f(x)$ in $\mathbb{Z}[x]/(x^{N} + 1)$. This preimage mapping has a particularly nice structure, in that you build it by repeating the polynomial’s coefficients twice and flipping the sign of the second half. It’s easy to see that the product $f^*(x) g^*(x)$ maps down to $4f(x)g(x)$.

So if we properly account for these extra constant factors floating around, our strategy to perform negacyclic polynomial multiplication is to map $f$ and $g$ up to the larger ring as described, compute their cyclic product (modulo $x^{2N} – 1$) using the FFT, and then the result should be a degree $2N-1$ polynomial which can be reduced with one more modular reduction step to the right degree $N-1$ negacyclic product, i.e., setting $x^N = -1$, which materializes as taking the second half of the coefficients, flipping their signs, and adding them to the corresponding coefficients in the first half.

The code for this is:

def negacyclic_polymul_preimage_and_map_back(p1, p2):
    p1_preprocessed = numpy.concatenate([p1, -p1])
    p2_preprocessed = numpy.concatenate([p2, -p2])
    product = fft(p1_preprocessed) * fft(p2_preprocessed)
    inverted = ifft(product)
    rounded = numpy.round(numpy.real(inverted)).astype(p1.dtype)
    return (rounded[: p1.shape[0]] - rounded[p1.shape[0] :]) // 4

However, this chosen mapping hides another clever trick. The product of the two preimages has enough structure that we can “read” the result off without doing the full “set $x^N = -1$” reduction step. Mapping $f$ and $g$ up to $f^*, g^*$ and taking their product modulo $(x^{2N} – 1)$ gives

\[ \begin{aligned} f^*g^* &= -f(x^N-1) \cdot -g(x^N – 1) \\ &= fg (x^N-1)^2 \\ &= fg(x^{2N} – 2x^N + 1) \\ &= fg(2 – 2x^N) \\ &= 2(fg – x^Nfg) \end{aligned} \]

This has the same syntactical format as the original mapping $f \mapsto f – x^Nf$, with an extra factor of 2, and so its coefficients also have the form “repeat the coefficients and flip the sign of the second half” (times two). We can then do the “inverse mapping” by reading only the first half of the coefficients and dividing by 2.

def negacyclic_polymul_use_special_preimage(p1, p2):
    p1_preprocessed = numpy.concatenate([p1, -p1])
    p2_preprocessed = numpy.concatenate([p2, -p2])
    product = fft(p1_preprocessed) * fft(p2_preprocessed)
    inverted = ifft(product)
    rounded = numpy.round(0.5 * numpy.real(inverted)).astype(p1.dtype)
    return rounded[: p1.shape[0]]

Our chosen mapping $f \mapsto f-x^Nf$ is not particularly special, except that it uses a small number of pre and post-processing operations. For example, if you instead used the mapping $f \mapsto 2f + x^Nf$ (which would map back to $f$ exactly), then the FFT product would result in $5fg + 4x^Nfg$ in the larger ring. You can still read off the coefficients as before, but you’d have to divide by 5 instead of 2 (which, the superstitious would say, is harder). It seems that “double and negate” followed by “halve and take first half” is the least amount of pre/post processing possible.

Negacyclic polynomials with a “twist”

The previous section identified a nice mapping (or embedding) of the input polynomials into a larger ring. But studying that shows some symmetric structure in the FFT output. I.e., the coefficients of $f$ and $g$ are repeated twice, with some scaling factors. It also involves taking an FFT of two $2N$-dimensional vectors when we start from two $N$-dimensional vectors.

This sort of situation should make you think that we can do this more efficiently, either by using a smaller size FFT or by packing some data into the complex part of the input, and indeed we can do both.

[Aside: it’s well known that if all the entries of an FFT input are real, then the result also has symmetry that can be exploted for efficiency by reframing the problem as a size-N/2 FFT in some cases, and just removing half the FFT algorithm’s steps in other cases, see Wikipedia for more]

This technique was explained in Fast multiplication and its applications (pdf link) by Daniel Bernstein, a prominent cryptographer who specializes in cryptography performance, and whose work appears in widely-used standards like TLS, OpenSSH, and he designed a commonly used elliptic curve for cryptography.

[Aside: Bernstein cites this technique as using something called the “Tangent FFT (pdf link).” This is a drop-in FFT replacement he invented that is faster than previous best (split-radix FFT), and Bernstein uses it mainly to give a precise expression for the number of operations required to do the multiplication end to end. We will continue to use the numpy FFT implementation, since in this article I’m just focusing on how to express negacyclic multiplication in terms of the FFT. Also worth noting both the Tangent FFT and “Fast multiplication” papers frame their techniques—including FFT algorithm implementations!—in terms of polynomial ring factorizations and mappings. Be still, my beating cardioid.]

In terms of polynomial mappings, we start from the ring $\mathbb{R}[x] / (x^N + 1)$, where $N$ is a power of 2. We then pick a reversible mapping from $\mathbb{R}[x]/(x^N + 1) \to \mathbb{C}[x]/(x^{N/2} – 1)$ (note the field change from real to complex), apply the FFT to the image of the mapping, and reverse appropriately it at the end.

One such mapping takes two steps, first mapping $\mathbb{R}[x]/(x^N + 1) \to \mathbb{C}[x]/(x^{N/2} – i)$ and then from $\mathbb{C}[x]/(x^{N/2} – i) \to \mathbb{C}[x]/(x^{N/2} – 1)$. The first mapping is as easy as the last section, because $(x^N + 1) = (x^{N/2} + i) (x^{N/2} – i)$, and so we can just set $x^{N/2} = i$ and reduce the polynomial. This as the effect of making the second half of the polynomial’s coefficients become the complex part of the first half of the coefficients.

The second mapping is more nuanced, because we’re not just reducing via factorization. And we can’t just map $i \mapsto 1$ generically, because that would reduce complex numbers down to real values. Instead, we observe that (momentarily using an arbitrary degree $k$ instead of $N/2$), for any polynomial $f \in \mathbb{C}[x]$, the remainder of $f \mod x^k-i$ uniquely determines the remainder of $f \mod x^k – 1$ via the change of variables $x \mapsto \omega_{4k} x$, where $\omega_{4k}$ is a $4k$-th primitive root of unity $\omega_{4k} = e^{\frac{2 \pi i}{4k}}$. Spelling this out in more detail: if $f(x) \in \mathbb{C}[x]$ has remainder $f(x) = g(x) + h(x)(x^k – i)$ for some polynomial $h(x)$, then

\[ \begin{aligned} f(\omega_{4k}x) &= g(\omega_{4k}x) + h(\omega_{4k}x)((\omega_{4k}x)^{k} – i) \\ &= g(\omega_{4k}x) + h(\omega_{4k}x)(e^{\frac{\pi i}{2}} x^k – i) \\ &= g(\omega_{4k}x) + i h(\omega_{4k}x)(x^k – 1) \\ &= g(\omega_{4k}x) \mod (x^k – 1) \end{aligned} \]

Translating this back to $k=N/2$, the mapping from $\mathbb{C}[x]/(x^{N/2} – i) \to \mathbb{C}[x]/(x^{N/2} – 1)$ is $f(x) \mapsto f(\omega_{2N}x)$. And if $f = f_0 + f_1x + \dots + f_{N/2 – 1}x^{N/2 – 1}$, then the mapping involves multiplying each coefficient $f_k$ by $\omega_{2N}^k$.

When you view polynomials as if they were a simple vector of their coefficients, then this operation $f(x) \mapsto f(\omega_{k}x)$ looks like $(a_0, a_1, \dots, a_n) \mapsto (a_0, \omega_{k} a_1, \dots, \omega_k^n a_n)$. Bernstein calls the operation a twist of $\mathbb{C}^n$, which I mused about in this Mathstodon thread.

What’s most important here is that each of these transformations are invertible. The first because the top half coefficients end up in the complex parts of the polynomial, and the second because the mapping $f(x) \mapsto f(\omega_{2N}^{-1}x)$ is an inverse. Together, this makes the preprocessing and postprocessing exact inverses of each other. The code is then

def negacyclic_polymul_complex_twist(p1, p2):
    n = p2.shape[0]
    primitive_root = primitive_nth_root(2 * n)
    root_powers = primitive_root ** numpy.arange(n // 2)

    p1_preprocessed = (p1[: n // 2] + 1j * p1[n // 2 :]) * root_powers
    p2_preprocessed = (p2[: n // 2] + 1j * p2[n // 2 :]) * root_powers

    p1_ft = fft(p1_preprocessed)
    p2_ft = fft(p2_preprocessed)
    prod = p1_ft * p2_ft
    ifft_prod = ifft(prod)
    ifft_rotated = ifft_prod * primitive_root ** numpy.arange(0, -n // 2, -1)

    return numpy.round(
        numpy.concatenate([numpy.real(ifft_rotated), numpy.imag(ifft_rotated)])
    ).astype(p1.dtype)

And so, at the cost of a bit more pre- and postprocessing, we can negacyclically multiply two degree $N-1$ polynomials using an FFT of length $N/2$. In theory, no information is wasted and this is optimal.

And finally, a simple matrix multiplication

The last technique I wanted to share is not based on the FFT, but it’s another method for doing negacyclic polynomial multiplication that has come in handy in situations where I am unable to use FFTs. I call it the Toeplitz method, because one of the polynomials is converted to a Toeplitz matrix. Sometimes I hear it referred to as a circulant matrix technique, but due to the negacyclic sign flip, I don’t think it’s a fully accurate term.

The idea is to put the coefficients of one polynomial $f(x) = f_0 + f_1x + \dots + f_{N-1}x^{N-1}$ into a matrix as follows:

\[ \begin{pmatrix} f_0 & -f_{N-1} & \dots & -f_1 \\ f_1 & f_0 & \dots & -f_2 \\ \vdots & \vdots & \ddots & \vdots \\ f_{N-1} & f_{N-2} & \dots & f_0 \end{pmatrix} \]

The polynomial coefficients are written down in the first column unchanged, then in each subsequent column, the coefficients are cyclically shifted down one, and the term that wraps around the top has its sign flipped. When the second polynomial is treated as a vector of its coefficients, say, $g(x) = g_0 + g_1x + \dots + g_{N-1}x^{N-1}$, then the matrix-vector product computes their negacyclic product (as a vector of coefficients):

\[ \begin{pmatrix} f_0 & -f_{N-1} & \dots & -f_1 \\ f_1 & f_0 & \dots & -f_2 \\ \vdots & \vdots & \ddots & \vdots \\ f_{N-1} & f_{N-2} & \dots & f_0 \end{pmatrix} \begin{pmatrix} g_0 \\ g_1 \\ \vdots \\ g_{N-1} \end{pmatrix} \]

This works because each row $j$ corresponds to one output term $x^j$, and the cyclic shift for that row accounts for the degree-wrapping, with the sign flip accounting for the negacyclic part. (If there were no sign attached, this method could be used to compute a cyclic polynomial product).

The Python code for this is

def cylic_matrix(c: numpy.array) -> numpy.ndarray:
    """Generates a cyclic matrix with each row of the input shifted.

    For input: [1, 2, 3], generates the following matrix:

        [[1 2 3]
         [2 3 1]
         [3 1 2]]
    """
    c = numpy.asarray(c).ravel()
    a, b = numpy.ogrid[0 : len(c), 0 : -len(c) : -1]
    indx = a + b
    return c[indx]


def negacyclic_polymul_toeplitz(p1, p2):
    n = len(p1)

    # Generates a sign matrix with 1s below the diagonal and -1 above.
    up_tri = numpy.tril(numpy.ones((n, n), dtype=int), 0)
    low_tri = numpy.triu(numpy.ones((n, n), dtype=int), 1) * -1
    sign_matrix = up_tri + low_tri

    cyclic_matrix = cylic_matrix(p1)
    toeplitz_p1 = sign_matrix * cyclic_matrix
    return numpy.matmul(toeplitz_p1, p2)

Obviously on most hardware this would be less efficient than an FFT-based method (and there is some relationship between circulant matrices and Fourier Transforms, see Wikipedia). But in some cases—when the polynomials are small, or one of the two polynomials is static, or a particular hardware choice doesn’t handle FFTs with high-precision floats very well, or you want to take advantage of natural parallelism in the matrix-vector product—this method can be useful. It’s also simpler to reason about.

Until next time!

Polynomial Multiplication Using the FFT

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Problem: Compute the product of two polynomials efficiently.

Solution:

import numpy
from numpy.fft import fft, ifft


def poly_mul(p1, p2):
    """Multiply two polynomials.

    p1 and p2 are arrays of coefficients in degree-increasing order.
    """
    deg1 = p1.shape[0] - 1
    deg2 = p1.shape[0] - 1
    # Would be 2*(deg1 + deg2) + 1, but the next-power-of-2 handles the +1
    total_num_pts = 2 * (deg1 + deg2)
    next_power_of_2 = 1 << (total_num_pts - 1).bit_length()

    ff_p1 = fft(numpy.pad(p1, (0, next_power_of_2 - p1.shape[0])))
    ff_p2 = fft(numpy.pad(p2, (0, next_power_of_2 - p2.shape[0])))
    product = ff_p1 * ff_p2
    inverted = ifft(product)
    rounded = numpy.round(numpy.real(inverted)).astype(numpy.int32)
    return numpy.trim_zeros(rounded, trim='b')

Discussion: The Fourier Transform has a lot of applications to science, and I’ve covered it on this blog before, see the Signal Processing section of Main Content. But it also has applications to fast computational mathematics.

The naive algorithm for multiplying two polynomials is the “grade-school” algorithm most readers will already be familiar with (see e.g., this page), but for large polynomials that algorithm is slow. It requires $O(n^2)$ arithmetic operations to multiply two polynomials of degree $n$.

This short tip shows a different approach, which is based on the idea of polynomial interpolation. As a side note, I show the basic theory of polynomial interpolation in chapter 2 of my book, A Programmer’s Introduction to Mathematics, along with an application to cryptography called “Secret Sharing.”

The core idea is that given $n+1$ distinct evaluations of a polynomial $p(x)$ (i.e., points $(x, p(x))$ with different $x$ inputs), you can reconstruct the coefficients of $p(x)$ exactly. And if you have two such point sets for two different polynomials $p(x), q(x)$, a valid point set of the product $(pq)(x)$ is the product of the points that have the same $x$ inputs.

\[ \begin{aligned} p(x) &= \{ (x_0, p(x_0)), (x_1, p(x_1)), \dots, (x_n, p(x_n)) \} \\ q(x) &= \{ (x_0, q(x_0)), (x_1, q(x_1)), \dots, (x_n, q(x_n)) \} \\ (pq)(x) &= \{ (x_0, p(x_0)q(x_0)), (x_1, p(x_1)q(x_1)), \dots, (x_n, p(x_n)q(x_n)) \} \end{aligned} \]

The above uses $=$ loosely to represent that the polynomial $p$ can be represented by the point set on the right hand side.

So given two polynomials $p(x), q(x)$ in their coefficient forms, one can first convert them to their point forms, multiply the points, and then reconstruct the resulting product.

The problem is that the two conversions, both to and from the coefficient form, are inefficient for arbitrary choices of points $x_0, \dots, x_n$. The trick comes from choosing special points, in such a way that the intermediate values computed in the conversion steps can be reused. This is where the Fourier Transform comes in: choose $x_0 = \omega_{N}$, the complex-N-th root of unity, and $x_k = \omega_N^k$ as its exponents. $N$ is required to be large enough so that $\omega_N$’s exponents have at least $2n+1$ distinct values required for interpolating a degree-at-most-$2n$ polynomial, and because we’re doing the Fourier Transform, it will naturally be “the next largest power of 2” bigger than the degree of the product polynomial.

Then one has to observe that, by its very formula, the Fourier Transform is exactly the evaluation of a polynomial at the powers of the $N$-th root of unity! In formulas: if $a = (a_0, \dots, a_{n-1})$ is a list of real numbers define $p_a(x) = a_0 + a_1x + \dots + a_{n-1}x^{n-1}$. Then $\mathscr{F}(a)(k)$, the Fourier Transform of $a$ at index $k$, is equal to $p_a(\omega_n^k)$. These notes by Denis Pankratov have more details showing that the Fourier Transform formula is a polynomial evaluation (see Section 3), and this YouTube video by Reducible also has a nice exposition. This interpretation of the FT as polynomial evaluation seems to inspire quite a few additional techniques for computing the Fourier Transform that I plan to write about in the future.

The last step is to reconstruct the product polynomial from the product of the two point sets, but because the Fourier Transform is an invertible function (and linear, too), the inverse Fourier Transform does exactly that: given a list of the $n$ evaluations of a polynomial at $\omega_n^k, k=0, \dots, n-1$, return the coefficients of the polynomial.

This all fits together into the code above:

  1. Pad the input coefficient lists with zeros, so that the lists are a power of 2 and at least 1 more than the degree of the output product polynomial.
  2. Compute the FFT of the two padded polynomials.
  3. Multiply the result pointwise.
  4. Compute the IFFT of the product.
  5. Round the resulting (complex) values back to integers.

Hey, wait a minute! What about precision issues?

They are a problem when you have large numbers or large polynomials, because the intermediate values in steps 2-4 can lose precision due to the floating point math involved. This short note of Richard Fateman discusses some of those issues, and two paths forward include: deal with it somehow, or use an integer-exact analogue called the Number Theoretic Transform (which itself has issues I’ll discuss in a future, longer article).

Postscript: I’m not sure how widely this technique is used. It appears the NTL library uses the polynomial version of Karatsuba multiplication instead (though it implements FFT elsewhere). However, I know for sure that much software involved in doing fully homomorphic encryption rely on the FFT for performance reasons, and the ones that don’t instead use the NTT.

“Practical Math” Preview: Collect Sensitive Survey Responses Privately

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This is a draft of a chapter from my in-progress book, Practical Math for Programmers: A Tour of Mathematics in Production Software.

Tip: Determine an aggregate statistic about a sensitive question, when survey respondents do not trust that their responses will be kept secret.

Solution:

import random

def respond_privately(true_answer: bool) -> bool:
    '''Respond to a survey with plausible deniability about your answer.'''
    be_honest = random.random() < 0.5
    random_answer = random.random() < 0.5
    return true_answer if be_honest else random_answer

def aggregate_responses(responses: List[bool]) -> Tuple[float, float]:
    '''Return the estimated fraction of survey respondents that have a truthful
    Yes answer to the survey question.
    '''
    yes_response_count = sum(responses)
    n = len(responses)
    mean = 2 * yes_response_count / n - 0.5
    # Use n-1 when estimating variance, as per Bessel's correction.
    variance = 3 / (4 * (n - 1))
    return (mean, variance)

In the late 1960’s, most abortions were illegal in the United States. Daniel G. Horvitz, a statistician at The Research Triangle Institute in North Carolina and a leader in survey design for social sciences, was tasked with estimating how many women in North Carolina were receiving illegal abortions. The goal was to inform state and federal policymakers about the statistics around abortions, many of which were unreported, even when done legally.

The obstacles were obvious. As Horvitz put it, “a prudent woman would not divulge to a stranger the fact that she was party to a crime for which she could be prosecuted.” [Abernathy70] This resulted in a strong bias in survey responses. Similar issues had plagued surveys of illegal activity of all kinds, including drug abuse and violent crime. Lack of awareness into basic statistics about illegal behavior led to a variety of misconceptions, such as that abortions were not frequently sought out.

Horvitz worked with biostatisticians James Abernathy and Bernard Greenberg to test out a new method to overcome this obstacle, without violating the respondent’s privacy or ability to plausibly deny illegal behavior. The method, called randomized response, was invented by Stanley Warner in 1965, just a few years earlier. [Warner65] Warner’s method was a bit different from what we present in this Tip, but both Warner’s method and the code sample above use the same strategy of adding randomization to the survey.

The mechanism, as presented in the code above, requires respondents to start by flipping a coin. If heads, they answer the sensitive question truthfully. If tails, they flip a second coin to determine how to answer the question—heads resulting in a “yes” answer, tails in a “no” answer. Naturally, the coin flips are private and controlled by the respondent. And so if a respondent answers “Yes” to the question, they may plausibly claim the “Yes” was determined by the coin, preserving their privacy. The figure below describes this process as a diagram.

A branching diagram showing the process a survey respondent takes to record their response.

Another way to describe the outcome is to say that each respondent’s answer is a single bit of information that is flipped with probability 1/4. This is half way between two extremes on the privacy/accuracy tradeoff curve. The first extreme is a “perfectly honest” response, where the bit is never flipped and all information is preserved. The second extreme has the bit flipped with probability 1/2, which is equivalent to ignoring the question and choosing your answer completely at random, losing all information in the aggregate responses. In this perspective, the aggregate survey responses can be thought of as a digital signal, and the privacy mechanism adds noise to that signal.

It remains to determine how to recover the aggregate signal from these noisy responses. In other words, the surveyor cannot know any individual’s true answer, but they can, with some extra work, estimate statistics about the underlying population by correcting for the statistical bias. This is possible because the randomization is well understood. The expected fraction of “Yes” answers can be written as a function of the true fraction of “Yes” answers, and hence the true fraction can be solved for. In this case, where the random coin is fair, that formula is as follows (where $ \mathbf{P}$ stands for “the probability of”).

$ \displaystyle \mathbf{P}(\textup{Yes answer}) = \frac{1}{2} \mathbf{P}(\textup{Truthful yes answer}) + \frac{1}{4}$

And so we solve for $ \mathbf{P}(\textup{Truthful yes answer})$

$ \displaystyle \mathbf{P}(\textup{Truthful yes answer}) = 2 \mathbf{P}(\textup{Yes answer}) – \frac{1}{2}$

We can replace the true probability $ \mathbf{P}(\textup{Yes answer})$ above with our fraction of “Yes” responses from the survey, and the result is an estimate $ \hat{p}$ of $ \mathbf{P}(\textup{Truthful yes answer})$. This estimate is unbiased, but has additional variance—beyond the usual variance caused by picking a finite random sample from the population of interest—introduced by the randomization mechanism.

With a bit of effort, one can calculate that the variance of the estimate is

$ \displaystyle \textup{Var}(\hat{p}) = \frac{3}{4n}$

And via Chebyshev’s inequality, which bounds the likelihood that an estimator is far away from its expectation, we can craft a confidence interval and determine the needed sample sizes. Specifically, the estimate $ \hat{p}$ has additive error at most $ q$ with probability at most $ \textup{Var}(\hat{p}) / q^2$. This implies that for a confidence of $ 1-c$, one requires at least $ n \geq 3 / (4 c q^2)$ samples. For example, to achieve error 0.01 with 90 percent confidence ($ c=0.1$), one requires 7,500 responses.

Horvitz’s randomization mechanism didn’t use coin flips. Instead they used an opaque box with red or blue colored balls which the respondent, who was in the same room as the surveyor, would shake and privately reveal a random color through a small window facing away from the surveyor. The statistical principle is the same. Horvitz and his associates surveyed the women about their opinions of the privacy protections of this mechanism. When asked whether their friends would answer a direct question about abortion honestly, over 80% either believed their friends would lie, or were unsure. [footnote: A common trick in survey methodology when asking someone if they would be dishonest is to instead ask if their friends would be dishonest. This tends to elicit more honesty, because people are less likely to uphold a false perception of the moral integrity of others, and people also don’t realize that their opinion of their friends correlates with their own personal behavior and attitudes. In other words, liars don’t admit to lying, but they think lying is much more common than it really is.] But 60% were convinced there was no trick involved in the randomization, while 20% were unsure and 20% thought there was a trick. This suggests many people were convinced that Horvitz’s randomization mechanism provided the needed safety guarantees to answer honestly.

Horvitz’s survey was a resounding success, both for randomized response as a method and for measuring abortion prevalence. [Abernathy70] They estimated the abortion rate at about 22 per 100 conceptions, with a distinct racial bias—minorities were twice as likely as whites to receive an abortion. Comparing their findings to a prior nationwide study from 1955—the so-called Arden House estimate—which gave a range of between 200,000 and 1.2 million abortions per year, Horvitz’s team estimated more precisely that there were 699,000 abortions in 1955 in the United States, with a reported standard deviation of about 6,000, less than one percent. For 1967, the year of their study, they estimated 829,000.

Their estimate was referenced widely in the flurry of abortion law and court cases that followed due to a surging public interest in the topic. For example, it is cited in the 1970 California Supreme Court opinion for the case Ballard v. Anderson, which concerned whether a minor needs parental consent to receive an otherwise legal abortion. [Ballard71, Roemer71] It was also cited in amici curiae briefs submitted to the United States Supreme Court in 1971 for Roe v. Wade, the famous case that invalidated most U.S. laws making abortion illegal. One such brief was filed jointly by the country’s leading women’s rights organizations like the National Organization for Women. Citing Horvitz for this paragraph, it wrote, [Womens71]

While the realities of law enforcement, social and public health problems posed by abortion laws have been openly discussed […] only within a period of not more than the last ten years, one fact appears undeniable, although unverifiable statistically. There are at least one million illegal abortions in the United States each year. Indeed, studies indicate that, if the local law still has qualifying requirements, the relaxation in the law has not diminished to any substantial extent the numbers in which women procure illegal abortions.

It’s unclear how the authors got this one million number (Horvitz’s estimate was 20% less for 1967), nor what they meant by “unverifiable statistically.” It may have been a misinterpretation of the randomized response technique. In any event, randomized response played a crucial role in providing a foundation for political debate.

Despite Horvitz’s success, and decades of additional research on crime, drug use, and other sensitive topics, randomized response mechanisms have been applied poorly. In some cases, the desired randomization is inextricably complex, such as when requiring a continuous random number. In these cases, a manual randomization mechanism is too complex for a respondent to use accurately. Trying to use software-assisted devices can help, but can also produce mistrust in the interviewee. See [Rueda16] for additional discussion of these pitfalls and what software packages exist for assisting in using randomized response. See [Fox16] for an analysis of the statistical differences between the variety of methods used between 1970 and 2010.

In other contexts, analogues to randomized response may not elicit the intended effect. In the 1950’s, Utah used death by firing squad as capital punishment. To avoid a guilty conscience of the shooters, one of five marksmen was randomly given a blank, providing him some plausible deniability that he knew he had delivered the killing shot. However, this approach failed on two counts. First, once a shot was fired the marksman could tell whether the bullet was real based on the recoil. Second, a 20% chance of a blank was not enough to dissuade a guilty marksman from purposely missing. In the 1951 execution of Elisio Mares, all four real bullets missed the condemned man’s heart, hitting his chest, stomach, and hip. He died, but it was neither painless nor instant.

Of many lessons one might draw from the botched execution, one is that randomization mechanisms must take into account both the psychology of the participants as well as the severity of a failed outcome.

References

@book{Fox16,
  title = {{Randomized Response and Related Methods: Surveying Sensitive Data}},
  author = {James Alan Fox},
  edition = {2nd},
  year = {2016},
  doi = {10.4135/9781506300122},
}

@article{Abernathy70,
  author = {Abernathy, James R. and Greenberg, Bernard G. and Horvitz, Daniel G.
            },
  title = {{Estimates of induced abortion in urban North Carolina}},
  journal = {Demography},
  volume = {7},
  number = {1},
  pages = {19-29},
  year = {1970},
  month = {02},
  issn = {0070-3370},
  doi = {10.2307/2060019},
  url = {https://doi.org/10.2307/2060019},
}

@article{Warner65,
  author = {Stanley L. Warner},
  journal = {Journal of the American Statistical Association},
  number = {309},
  pages = {63--69},
  publisher = {{American Statistical Association, Taylor \& Francis, Ltd.}},
  title = {Randomized Response: A Survey Technique for Eliminating Evasive
           Answer Bias},
  volume = {60},
  year = {1965},
}

@article{Ballard71,
  title = {{Ballard v. Anderson}},
  journal = {California Supreme Court L.A. 29834},
  year = {1971},
  url = {https://caselaw.findlaw.com/ca-supreme-court/1826726.html},
}

@misc{Womens71,
  title = {{Motion for Leave to File Brief Amici Curiae on Behalf of Women’s
           Organizations and Named Women in Support of Appellants in Each Case,
           and Brief Amici Curiae.}},
  booktitle = {{Appellate Briefs for the case of Roe v. Wade}},
  number = {WL 128048},
  year = {1971},
  publisher = {Supreme Court of the United States},
}

@article{Roemer71,
  author = {R. Roemer},
  journal = {Am J Public Health},
  pages = {500--509},
  title = {Abortion law reform and repeal: legislative and judicial developments
           },
  volume = {61},
  number = {3},
  year = {1971},
}

@incollection{Rueda16,
  title = {Chapter 10 - Software for Randomized Response Techniques},
  editor = {Arijit Chaudhuri and Tasos C. Christofides and C.R. Rao},
  series = {Handbook of Statistics},
  publisher = {Elsevier},
  volume = {34},
  pages = {155-167},
  year = {2016},
  booktitle = {Data Gathering, Analysis and Protection of Privacy Through
               Randomized Response Techniques: Qualitative and Quantitative Human
               Traits},
  doi = {https://doi.org/10.1016/bs.host.2016.01.009},
  author = {M. Rueda and B. Cobo and A. Arcos and R. Arnab},
}

The Gadget Decomposition in FHE

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Lately I’ve been studying Fully Homomorphic Encryption, which is the miraculous ability to perform arbitrary computations on encrypted data without learning any information about the underlying message. It’s the most comprehensive private computing solution that can exist (and it does exist!).

The first FHE scheme by Craig Gentry was based on ideal lattices and was considered very complex (I never took the time to learn how it worked). Some later schemes (GSW = Gentry-Sahai-Waters) are based on matrix multiplication, and are conceptually much simpler. Even more recent FHE schemes build on GSW or use it as a core subroutine.

All of these schemes inject random noise into the ciphertext, and each homomorphic operation increases noise. Once the noise gets too big, you can no longer decrypt the message, and so every now and then you must apply a process called “bootstrapping” that reduces noise. It also tends to be the performance bottleneck of any FHE scheme, and this bottleneck is why FHE is not considered practical yet.

To help reduce noise growth, many FHE schemes like GSW use a technical construction dubbed the gadget decomposition. Despite the terribly vague name, it’s a crucial limitation on noise growth. When it shows up in a paper, it’s usually remarked as “well known in the literature,” and the details you’d need to implement it are omitted. It’s one of those topics.

So I’ll provide some details. The code from this post is on GitHub.

Binary digit decomposition

To create an FHE scheme, you need to apply two homomorphic operations to ciphertexts: addition and multiplication. Most FHE schemes admit one of the two operations trivially. If the ciphertexts are numbers as in RSA, you multiply them as numbers and that multiplies the underlying messages, but addition is not known to be possible. If ciphertexts are vectors as in the “Learning With Errors” scheme (LWE)—the basis of many FHE schemes—you add them as vectors and that adds the underlying messages. (Here the “Error” in LWE is synonymous with “random noise”, I will use the term “noise”) In LWE and most FHE schemes, a ciphertext hides the underlying message by adding random noise, and addition of two ciphertexts adds the corresponding noise. After too many unmitigated additions, the noise will grow so large it obstructs the message. So you stop computing, or you apply a bootstrapping operation to reduce the noise.

Most FHE schemes also allow you to multiply a ciphertext by an unencrypted constant $ A$, but then the noise scales by a factor of $ A$, which is undesirable if $ A$ is large. So you either need to limit the coefficients of your linear combinations by some upper bound, or use a version of the gadget decomposition.

The simplest version of the gadget decomposition works like this. Instead of encrypting a message $ m \in \mathbb{Z}$, you would encrypt $ m, 2m, 4m, …, 2^{k-1} m$ for some choice of $ k$, and then to multiply $ A < 2^k$ you write the binary digits of $ A = \sum_{i=0}^{k-1} a_i 2^i$ and you compute $ \sum_{i=0}^{k-1} a_i \textup{Enc}(2^i m)$. If the noise in each encryption is $ E$, and summing ciphertexts sums noise, then this trick reduces the noise growth from $ O(AE)$ to $ O(kE) = O(\log(A)E)$, at the cost of tracking $ k$ ciphertexts. (Calling the noise $ E$ is a bit of an abuse—in reality the error is sampled from a random distribution—but hopefully you see my point).

Some folks call the mapping $ \textup{PowersOf2}(m) = m \cdot (2^0, 2^1, 2^2, \dots, 2^{k-1})$, and for the sake of this article let’s call the operation of writing a number $ A$ in terms of its binary digits $ \textup{Bin}(A) = (a_0, \dots, a_{k-1})$ (note, the first digit is the least-significant bit, i.e., it’s a little-endian representation). Then PowersOf2 and Bin expand an integer product into a dot product, while shifting powers of 2 from one side to the other.

$ \displaystyle A \cdot m = \langle \textup{Bin}(A), \textup{PowersOf2}(m) \rangle$

This inspired the following “proof by meme” that I can’t resist including.

Working out an example, if the message is $ m=7$ and $ A = 100, k=7$, then $ \textup{PowersOf2}(7) = (7, 14, 28, 56, 112, 224, 448, 896)$ and $ \textup{Bin}(A) = (0,0,1,0,0,1,1,0)$ (again, little-endian), and the dot product is

$ \displaystyle 28 \cdot 1 + 224 \cdot 1 + 448 \cdot 1 = 700 = 7 \cdot 2^2 + 7 \cdot 2^5 + 7 \cdot 2^6$

A generalized gadget construction

One can generalize the binary digit decomposition to different bases, or to vectors of messages instead of a single message, or to include a subset of the digits for varying approximations. I’ve been puzzling over an FHE scheme that does all three. In my search for clarity I came across a nice paper of Genise, Micciancio, and Polyakov called “Building an Efficient Lattice Gadget Toolkit: Subgaussian Sampling and More“, in which they state a nice general definition.

Definition: For any finite additive group $ A$, an $ A$-gadget of size $ w$ and quality $ \beta$ is a vector $ \mathbf{g} \in A^w$ such that any group element $ u \in A$ can be written as an integer combination $ u = \sum_{i=1}^w g_i x_i$ where $ \mathbf{x} = (x_1, \dots , x_w)$ has norm at most $ \beta$.

The main groups considered in my case are $ A = (\mathbb{Z}/q\mathbb{Z})^n$, where $ q$ is usually $ 2^{32}$ or $ 2^{64}$, i.e., unsigned int sizes on computers for which we get free modulus operations. In this case, a $ (\mathbb{Z}/q\mathbb{Z})^n$-gadget is a matrix $ G \in (\mathbb{Z}/q\mathbb{Z})^{n \times w}$, and the representation $ x \in \mathbb{Z}^w$ of $ u \in (\mathbb{Z}/q\mathbb{Z})^n$ satisfies $ Gx = u$.

Here $ n$ and $ q$ are fixed, and $ w, \beta$ are traded off to make the chosen gadget scheme more efficient (smaller $ w$) or better at reducing noise (smaller $ \beta$). An example of how this could work is shown in the next section by generalizing the binary digit decomposition to an arbitrary base $ B$. This allows you to use fewer digits to represent the number $ A$, but each digit may be as large as $ B$ and so the quality is $ \beta = O(B\sqrt{w})$.

One commonly-used construction is to convert an $ A$-gadget to an $ A^n$-gadget using the Kronecker product. Let $ g \in A^w$ be an $ A$-gadget of quality $ \beta$. Then the following matrix is an $ A^n$-gadget of size $ nw$ and quality $ \sqrt{n} \beta$:

$ \displaystyle G = I_n \otimes \mathbf{g}^\top = \begin{pmatrix} g_1 & \dots & g_w & & & & & & & \\ & & & g_1 & \dots & g_w & & & & \\ & & & & & & \ddots & & & \\ & & & & & & & g_1 & \dots & g_w \end{pmatrix}$

Blank spaces represent zeros, for clarity.

An example with $ A = (\mathbb{Z}/16\mathbb{Z})$. The $ A$-gadget is $ \mathbf{g} = (1,2,4,8)$. This has size $ 4 = \log(q)$ and quality $ \beta = 2 = \sqrt{1+1+1+1}$. Then for an $ A^3$-gadget, we construct

Now given a vector $ (15, 4, 7) \in \mathbb{A}^3$ we write it as follows, where each little-endian representation is concatenated into a single vector.

$ \displaystyle \mathbf{x} = \begin{pmatrix} 1\\1\\1\\1\\0\\0\\1\\0\\1\\1\\1\\0 \end{pmatrix}$

And finally,

To use the definition more rigorously, if we had to write the matrix above as a gadget “vector”, it would be in column order from left to right, $ \mathbf{g} = ((1,0,0), (2,0,0), \dots, (0,0,8)) \in A^{wn}$. Since the vector $ \mathbf{x}$ can be at worst all 1’s, its norm is at most $ \sqrt{12} = \sqrt{nw} = \sqrt{n} \beta = 2 \sqrt{3}$, as claimed above.

A signed representation in base B

As we’ve seen, the gadget decomposition trades reducing noise for a larger ciphertext size. With integers modulo $ q = 2^{32}$, this can be fine-tuned a bit more by using a larger base. Instead of PowersOf2 we could define PowersOfB, where $ B = 2^b$, such that $ B$ divides $ 2^{32}$. For example, with $ b = 8, B = 256$, we would only need to track 4 ciphertexts. And the gadget decomposition of the number we’re multiplying by would be the little-endian digits of its base-$ B$ representation. The cost here is that the maximum entry of the decomposed representation is 255.

We can fine tune this a little bit more by using a signed base-$ B$ representation. To my knowledge this is not the same thing as what computer programmers normally refer to as a signed integer, nor does it have anything to do with the two’s complement representation of negative numbers. Rather, instead of the normal base-$ B$ digits $ n_i \in \{ 0, 1, \dots, B-1 \}$ for a number $ N = \sum_{i=0}^k n_i B^i$, the signed representation chooses $ n_i \in \{ -B/2, -B/2 + 1, \dots, -1, 0, 1, \dots, B/2 – 1 \}$.

Computing the digits is slightly more involved, and it works by shifting large coefficients by $ -B/2$, and “absorbing” the impact of that shift into the next more significant digit. E.g., if $ B = 256$ and $ N = 2^{11} – 1$ (all 1s up to the 10th digit), then the unsigned little-endian base-$ B$ representation of $ N$ is $ (255, 7) = 255 + 7 \cdot 256$. The corresponding signed base-$ B$ representation subtracts $ B$ from the first digit, and adds 1 to the second digit, resulting in $ (-1, 8) = -1 + 8 \cdot 256$. This works in general because of the following “add zero” identity, where $ p$ and $ q$ are two successive unsigned digits in the unsigned base-$ B$ representation of a number.

$ \displaystyle \begin{aligned} pB^{k-1} + qB^k &= pB^{k-1} – B^k + qB^k + B^k \\ &= (p-B)B^{k-1} + (q+1)B^k \end{aligned}$

Then if $ q+1 \geq B/2$, you’d repeat and carry the 1 to the next higher coefficient.

The result of all this is that the maximum absolute value of a coefficient of the signed representation is halved from the unsigned representation, which reduces the noise growth at the cost of a slightly more complex representation (from an implementation standpoint). Another side effect is that the largest representable number is less than $ 2^{32}-1$. If you try to apply this algorithm to such a large number, the largest digit would need to be shifted, but there is no successor to carry to. Rather, if there are $ k$ digits in the unsigned base-$ B$ representation, the maximum number representable in the signed version has all digits set to $ B/2 – 1$. In our example with $ B=256$ and 32 bits, the largest digit is 127. The formula for the max representable integer is $ \sum_{i=0}^{k-1} (B/2 – 1) B^i = (B/2 – 1)\frac{B^k – 1}{B-1}$.

max_digit = base // 2 - 1
max_representable = (max_digit 
    * (base ** (num_bits // base_log) - 1) // (base - 1)
)

A simple python implementation computes the signed representation, with code copied below, in which $ B=2^b$ is the base, and $ b = \log_2(B)$ is base_log.

def signed_decomposition(
  x: int, base_log: int, total_num_bits=32) -> List[int]:
    result = []
    base = 1 << base_log
    digit_mask = (1 << base_log) - 1
    base_over_2_threshold = 1 << (base_log - 1)
    carry = 0

    for i in range(total_num_bits // base_log):
        unsigned_digit = (x >> (i * base_log)) & digit_mask
        if carry:
            unsigned_digit += carry
            carry = 0

        signed_digit = unsigned_digit
        if signed_digit >= base_over_2_threshold:
            signed_digit -= base
            carry = 1
        result.append(signed_digit)

    return result

In a future article I’d like to demonstrate the gadget decomposition in action in a practical setting called key switching, which allows one to convert an LWE ciphertext encrypted with key $ s_1$ into an LWE ciphertext encrypted with a different key $ s_2$. This operation increases noise, and so the gadget decomposition is used to reduce noise growth. Key switching is used in FHE because some operations (like bootstrapping) have the side effect of switching the encryption key.

Until then!