14. Secure Multiparty Computation

Secure Multiparty Computation (MPC)

Suppose we have a function $f$ that takes $n$ inputs and produces $m$ outputs.

\[(y_1, \dots, y_m) = f(x_1, \dots, x_n).\]

$N$ parties $P_1, \dots, P_N$ are trying to evaluate this function with a protocol. Each $x_i$ is submitted by one of the parties, and each output $y_j$ will be given to one or more parties.

In secure multiparty computation (MPC), we wish to achieve some security functionalities.

  • Privacy: no party learns anything about any other party’s inputs, except for the information in the output.
  • Soundness: honest parties compute correct outputs.
  • Input independence: all parties must choose their inputs independently of other parties’ inputs.

Security must hold even if there is any adversarial behavior in the party.

Example: Secure Summation

Suppose we have $n$ parties $P_1, \dots, P_n$ with private values $x_1, \dots, x_n$. We would like to securely compute the sum $s = x_1 + \cdots + x_n$.

  1. Choose $M$ large enough so that $M > s$.
  2. $P_1$ samples $r \la \Z_M$ and computes $s_1 = r + x_1 \pmod M$ and sends it to $P_2$.
  3. In the same manner, $P_i$ computes $s_i = s_{i-1} + x_i \pmod M$ and sends it to $P_{i+1}$.
  4. As the final step, $s_n$ is returned to $P_1$, where he outputs $s = s_n - r \pmod M$.

This protocol seems secure since $r$ is a random noise added to the actual partial sum. But the security actually depends on how we model adversarial behavior.

Consider the case where parties $P_2$ and $P_4$ team up (collusion). These two can share information between them. They have the following:

  • $P_2$ has $s_1$, $s_2$, $x_2$.
  • $P_4$ has $s_3$, $s_4$, $x_4$.

Using $s_2$ and $s_3$, they can compute $x_3 = s_3 - s_2$ and obtain the input of $P_3$. This violates privacy. Similarly, if $P_i$ and $P_j$ team up, the can compute the partial sum

\[s_{j - 1} - s_{i} = x_{i+1} + \cdots + x_{j-1}\]

which leaks information about the inputs of $P_{i+1}, \dots, P_{j-1}$.

Modeling Adversaries for Multiparty Computation

The adversary can decide not to follow the protocol and perform arbitrarily.

  • Semi-honest adversaries follows the protocol and tries to learn more information by inspecting the communication.
  • Malicious adversaries can behave in any way, unknown to us.

Semi-honest adversaries are similar to passive adversaries, whereas malicious adversaries are similar to active adversaries.

We can also model the corruption strategy. Some parties can turn into an adversary during the protocol.

  • In static corruptions, the set of adversarial parties is fixed throughout the execution.
  • In adaptive corruptions, the adversary corrupts parties during the execution, based on the information gained from the protocol execution.

We can decide how much computational power to give to the adversary. For computational security, an adversary must be efficient, only polynomial time strategies are allowed. For information-theoretic security, an adversary has unbounded computational power.

We will only consider semi-honest adversaries with static corruptions.

Defining Security for Multiparty Computation

The idea is the following.

An attack on the protocol in the real world is equivalent to some attack on the protocol in an ideal world in which no damage can be done.

In the ideal world, we use a trusted party to implement a protocol. All parties, both honest and corrupted, submit their input to the trusted party. Since the trusted party is not corrupted, the protocol is safe.

In the real world, there is no trusted party and parties must communicate with each other using a protocol.

Thus, a secure protocol must provide security in the real world that is equivalent to that in the ideal world. The definition is saying the following: there is no possible attack in the ideal world, so there is no possible attack in the real world. This kind of definition implies privacy, soundness and input independence.

For every efficient adversary $\mc{A}$ in the real world, there exists an equivalent efficient adversary $\mc{S}$ (usually called a simulator) in the ideal world.

Semi-Honest & Static Corruption

  • The view of a party consists of its input, random tape and the list of messages obtained from the protocol.
    • The view of an adversary is the union of views of corrupted parties.
    • If an adversary learned anything from the protocol, it must be efficiently computable from its view.
  • If a protocol is secure, it must be possible in the ideal world to generate something indistinguishable from the real world adversary’s view.
    • In the ideal world, the adversary’s view consists of inputs/outputs to and from the trusted party.
    • An adversary in the ideal world must be able to generate a view equivalent to the real world view. We call this ideal world adversary a simulator.
    • If we show the existence of a simulator, a real world adversary’s ability is the same as an adversary in the ideal world.

Definition. Let $\mc{A}$ be the set of parties that are corrupted, and let $\rm{Sim}$ be a simulator algorithm.

  • $\rm{Real}(\mc{A}; x_1, \dots, x_n)$: each party $P_i$ runs the protocol with private input $x_i$. Let $V_i$ be the final view of $P_i$. Output $\braces{V_i : i \in \mc{A}}$.
  • $\rm{Ideal}_\rm{Sim}(x_1, \dots, x_n)$: output $\rm{Sim}(\mc{A}; \braces{(x_i, y_i) : i \in \mc{A}})$.

A protocol is secure against semi-honest adversaries if there exists a simulator such that for every subset of corrupted parties $\mc{A}$, its views in the real and ideal worlds are indistinguishable.

Oblivious Transfer (OT)

This is a building block for building any MPC.

Suppose that the sender has data $m_1, \dots, m_n \in \mc{M}$, and the receiver has an index $i \in \braces{1, \dots, n}$. The sender wants to send exactly one message and hide others. Also, the receiver wants to hide which message he received.

This problem is called 1-out-of-$n$ oblivious transfer (OT).

1-out-of-2 OT Construction from ElGamal Encryption

We show an example of 1-out-of-2 OT using the ElGamal encryptions scheme. We use a variant where a hash function is used in encryption.

It is known that $k$-out-of-$n$ OT is constructible from 1-out-of-2 OTs.

Suppose that the sender Alice has messages $x_0, x_1 \in \braces{0, 1}\conj$, and the receiver Bob has a choice $\sigma \in \braces{0, 1}$.

  1. Bob chooses $sk = \alpha \la \Z_q$ and computes ${} h = g^\alpha {}$, and chooses $h’ \la G$.
  2. Bob sets $pk_\sigma = h$ and $pk_{1-\sigma} = h’$ and sends $(pk_0, pk_1)$ to Alice.
  3. Alice encrypts each $x_i$ using $pk_i$, obtains two ciphertexts.
    • $\beta_0, \beta_1 \la \Z_q$.
    • $c_0 = \big( g^{\beta_0}, H(pk_0^{\beta_0}) \oplus x_0 \big)$, $c_1 = \big( g^{\beta_1}, H(pk_1^{\beta_1}) \oplus x_1 \big)$.
  4. Alice sends $(c_0, c_1)$ to Bob.
  5. Bob decrypts $c_\sigma$ with $sk$ to get $x_\sigma$.

Correctness is obvious.

Alice’s view contains the following: $x_0, x_1, pk_0, pk_1, c_0, c_1$. Among these, $pk_0, pk_1$ are the received values from Bob. But these are random group elements, so she learns nothing about $\sigma$. The simulator can choose two random group elements to simulate Alice.

Bob’s view contains the following: $\sigma, \alpha, g^\alpha, h’, c_0, c_1, x_\sigma$. He only knows one private key, so he only learns $x_\sigma$, under the DL assumption. (He doesn’t have the discrete logarithm for $h’$) The simulator must simulate $c_0, c_1$, so it encrypts $x_\sigma$ with $pk_\sigma$, and as for $x_{1-\sigma}$, a random message is encrypted with $pk_{1-\sigma}$. This works because the encryption scheme is semantically secure, meaning that it doesn’t reveal any information about the underlying message.

The above works for semi-honest parties. To prevent malicious behavior, we fix the protocol a bit.

  1. Alice sends a random $w \la G$ first.
  2. Bob must choose $h$ and $h’$ so that $hh’ = w$. $h$ is chosen the same way, and $h’ = wh\inv$ is computed.

The remaining steps are the same, except that Alice checks if $pk_0 \cdot pk_1 = w$.

Bob must choose $h, h’$ such that $hh’ = w$. If not, Bob can choose ${} \alpha’ \la \Z_q {}$ and set $h’ = g^{\alpha’}$, enabling him to decrypt both $c_0, c_1$, revealing $x_0, x_1$. Under the DL assumption, Bob cannot find the discrete logarithm of $h’$, which prevents malicious behavior.

1-out-of-$n$ OT Construction from ElGamal Encryption

Let $m_1, \dots, m_n \in \mc{M}$ be the messages to send, and let $i$ be an index. We will use ElGamal encryption on a cyclic group $G = \span{g}$ of prime order, with a hash function and a semantically secure symmetric cipher $(E_S, D_S)$.

  1. Alice chooses $\beta \la \Z_q$, computes $v \la g^\beta$ and sends $v$ to Bob.
  2. Bob chooses $\alpha \la \Z_q$, computes $u \la g^\alpha v^{-i}$ and sends $u$ to Alice.
  3. For $j = 1, \dots, n$, Alice computes the following.
    • Compute $u_j \la u \cdot v^j = g^\alpha v^{j-i}$ as the public key for the $j$-th message.
    • Encrypt $m_j$ as $(g^\beta, c_j)$, where $c_j \la E_S\big( H(g^\beta, u_j^\beta), m_j \big)$.
  4. Alice sends $(c_1, \dots, c_n)$ to Bob.
  5. Bob decrypts $c_i$ as follows.
    • Compute symmetric key $k \la H(v, v^\alpha)$ where $v = g^\beta$ from step $1$.
    • $m_i \la D_S(k, c_i)$.

Note that all ciphertexts $c_j$ were created from the same ephemeral key $\beta \in \Z_q$.

For correctness, we check that Bob indeed receives $m_i$ from the above protocol. Check that $u_i = u\cdot v^i = g^\alpha v^0 = g^\alpha$, then $u_i^\beta = g^{\alpha\beta} = v^\alpha$. Since $c_i = E_S\big( H(g^\beta, u_i^\beta), m_i \big) = E_S\big( H(v, v^\alpha), m_i \big)$, the decryption gives ${} m_i {}$.

Now is this oblivious? All that Alice sees is $u = g^\alpha v^{-i}$ from Bob. Since $\alpha \la \Z_q$, $u$ is uniformly distributed over elements of $G$. Alice learns no information about $i$.

As for Bob, we need the CDH assumption. Suppose that Bob can query $H$ on two different ciphertexts $c_{j_1}, c_{j_2}$. Then he knows

\[u_{j_1}^\beta/u_{j_2}^\beta = v^{\beta(j_1 - j_2)},\]

and by raising both to the $(j_1 - j_2)\inv$ power (inverse in $\Z_q$), he can compute $v^\beta = g^{\beta^2}$. Thus, Bob has computed $g^{\beta^2}$ from $g^\beta$, and this breaks the CDH assumption.1 Thus Bob cannot query $H$ on two points, and is unable to decrypt two ciphertexts. He only learns $m_i$.

OT for Computing $2$-ary Function with Finite Domain

We can use an OT for computing a $2$-ary function with finite domain.

Let $f : X_1 \times X_2 \ra Y$ be a deterministic function with $X_1$, $X_2$ both finite. There are two parties ${} P_1, P_2 {}$ with inputs $x_1, x_2$, and they want to compute $f(x_1, x_2)$ without revealing their input.

Then we can use $1$-out-of-$\abs{X_2}$ OT to securely compute $f(x_1, x_2)$. Without loss of generality, suppose that $P_1$ is the sender.

${} P_1$ computes $y_x =f(x_1, x)$ for all $x \in X_2$, resulting in $\abs{X_2}$ messages. Then $P_1$ performs 1-out-of-$\abs{X_2}$ OT with $P_2$. The value of $x_2$ will be used as the choice of $P_2$, which will be oblivious to $P_1$.2

This method is inefficient, so we have better methods!

  1. Given $g^\alpha, g^\beta$, compute $g^{\alpha + \beta}$. Then compute $g^{\alpha^2}, g^{\beta^2}, g^{(\alpha+\beta)^2}$, and obtain $g^{2\alpha\beta}$. Exponentiate by $2\inv \in \Z_q$ to find $g^{\alpha\beta}$. 

  2. Can $P_1$ learn the value of $x_2$ from the final output $y_{x_2} = f(x_1, x_2)$? 

This post is licensed under CC BY 4.0 by the author.