Consider the problem where we have $l$ parties with messages $m_i$ who wish to generate the list $(m_{\pi(1)}, \dots, m_{\pi(l)})$, where $\pi$ is a permutation over the integer range $[1,\dots,l]$. Suppose further that they wish to do this without revealing $\pi$. This problem is essentially what mixnets were designed to solve. They send the sender or provider of a series of messages received in sequence by shuffling them around and about before forwarding them along. In doing so, the mixnet hides the mapping between the $i$-th input message and the $i$-th output message. In addition to permuting messages before forwarding them to their recipients, mixnets usually also perform some type of decryption or re-encryption so that the input and output messages are more difficult to correlate. One ideal property for these mixnets is that they work on opaque ciphertexts. In other words, it would be nice if the permutation and translation (decryption or re-encryption) steps operated over ciphertexts without having to observe the message plaintexts or know about the recipient of each message. In fact, this is not farfetched at all. Adida et al. [1] show that any homomorphic encryption scheme can be used to build an inefficient decryption and re-encryption shuffling schemes by obfuscation. They also show how to construct efficient shuffling schemes based on the BGN and Paillier encryption schemes. In this post, I’ll review outline fundamental idea behind their shuffle algorithm and then describe the basic Paillier cryptosystem. I will detail the generalized Paillier cryptosystem and how it’s used for shuffling in a subsequent post.
Let $m = [m_1,\dots,m_l]$ be a vector of $l$ messages we wish to permute by some permutation $\pi$. An easy way to do this is to generate a permutation matrix $\mathbf{A}$ where the rows are permuted according to $\pi$. The matrix-vector product $m’ = \mathbf{A}m$ then returns $m$ permuted under $\pi$. Let’s take a closer look at how $m’_i$ is computed. By the matrix-vector multiplication algorithm, it holds that
\[\begin{align} m'_i = \sum_{i}^{l} \mathbf{A}_{j, i} m_i \end{align}\]What if the entries of $\mathbf{A}$ and $m$ were encrypted under a multiplicative homomorphic encryption scheme? The product of two encryptions would be equal to the encryption of the product, e.g., $\mathbf{A}_{j,i}m_i$. Multiplying by an encrypted “1” is sort of like re-encrypting the input. Moreover, decryption does not reveal $\pi$ if the encryption scheme is semantically secure. Intuitively, this is because the encryption reveals nothing about the plaintext, i.e., the inputs from $\mathbf{A}$.
The authors of [1] use the additive and multiplicative properties of the BGN encryption scheme to implement a decryption shuffle algorithm, i.e., one in which decrypts and then outputs a permutation of its inputs. Personally, I find re-encryption shuffling algorithms to be more intriguing since they do not require the messages to be decrypted. This lets the operation be done by an untrusted proxy. As such, I’ll focus the remainder of this post on illustrating their re-encryption shuffle algorithm with some running code. But first, we need to do some review.
The Paillier encryption system is a tuple of algorithms $(\mathsf{G}, \mathsf{E}, \mathsf{G})$ for key generation, encryption, and decryption defined as follows (adapted from [1]).
To show correctness, assume we encrypted a message $m$ with the public key $pk$ and retrieved the ciphertext $c$. We would expect for the output of the decryption algorithm given $c$ and the secret key $sk$ to yield $p = m$. Let’s walk through the decryption computation.
Compute $c^{\lambda} = (g^mr^n)^{\lambda} = g^{m\lambda}r^{n\lambda} = g^{m\lambda}$. By Carmichael’s Theorem [2], $r^{n\lambda} \equiv 1 \mod n^2$.
Compute $g^{m\lambda} = (1 + n)^{m\lambda} = (1 + mn\lambda) \mod n^2$
Apply $L(x)$ to compute:
The following code implements this standard Paillier cryptosystem. Hopefully, in a following post, I’ll have time to describe how to generalize this algorithm to work in higher-order groups.