 # Discrete Logarithm

Created: 2023-03-29
Updated: 2023-08-26

Discrete logarithm is a fundamental concept in modern cryptography, with numerous applications in key exchange, digital signatures, and other cryptographic protocols.

This post explores the basics of discrete logarithm and some of the most important cryptographic protocols derived from it.

## Discrete Logarithm

Given `g` a generator of `G`, we set `o = ord(g)`.

The modular exponential function is defined as a function that maps numbers from `{0, 1, .., ord(n)-1 }` to `G`:

``````exp: Zo → G
exp(i): i → gⁱ mod n
``````

When applied to a generator of a cyclic group, modular exponentiation is injective and surjective (by definition of generator), we can thus define the inverse function.

The discrete logarithm function is defined as:

``````ind: G → Zo
ind(x): x → i , with x = gⁱ mod n for some i ∈ Zo
``````

Discrete logarithm is not a monotonic function, and currently we don’t know any efficient algorithm to compute it.

Proposition. Given `x, y ∈ Zn*` and a generator `g`:

``````ind(x·y) = (ind(x) + ind(y)) mod φ(n)
``````

This is a consequence of Little Fermat’s theorem, allowing to reduce the exponent modulo `φ(n)`.

Proof

``````ind(x) ≡ A (mod n), ind(y) ≡ B (mod n)

→ x ≡ gᴬ (mod n) , y ≡ gᴮ (mod n)
→ x·y ≡ gᴬ⁺ᴮ ≡ g^[(A + B) mod φ(n)] (mod n)
→ ind(x·y) = (A + B) mod φ(n)
→ ind(x·y) = (ind(x) + ind(y)) mod φ(n)
``````

### Notation

Based on what is the group operation, the practical details of the discrete logarithm function changes, but the semantics is the same: how many times we apply the group operation to the generator.

Exponential notation is used when the group operation is multiplication (e.g. multiplication of scalars in some prime group):

``````x = gᵏ = g·..·g
``````

Multiplicative notation is used when the group operation is addition (e.g. addition of points in some elliptic curve group):

``````x = k·g = g+..+g
``````

In both cases we write `ind(x) = k`

Follows the description of some of the more important cryptographic primitives and schemes which rely on the discrete logarithm problem hardness.

To be abstract and generic, if not specifically required, the group operation is not specified, and exponential notation is used.

Where we have to manipulate a message `m` we assume an invertible method to map it from the message domain to the group domain `G`. For example, we need to interpret `m` as a number in `Zp*` or as a point in an elliptic curve.

We may require to map the output of an operation in `G` to a value in `|G|`. In this case we define the abstract function `map_to_group_ord: G → |G|`.

For example:

• `G` is an elliptic curve and `a` a point: `map_to_group_ord(a) = a.x mod |G|`
• `G` is `Zp*` and `a` a scalar: `map_to_group_ord(a) = a mod |G|`

### Discrete Logarithm Problem

The discrete logarithm problem (DLP) involves finding the exponent to which a given group element must be raised to obtain another group element, within a specific mathematical group.

The security of all the schemes in this post is attributed to the computational hardness of solving the discrete logarithm problem and the lack of efficient solution techniques.

## ElGamal Scheme

Parameters:

• `G`: cyclic group with order `n`
• `g ∈ G`: a generator for `G`
• `x ∈ Zn*`: secret key
• `y ∈ G`: public key such that `y = gˣ`

Note that as a general rule we never choose as secret key `1` or `n` since these are trivially identifiable (by definition of generator):

``````g¹ = g  and  gⁿ = 1
``````

### Cipher

Encryption

Given a message `m ∈ G`, pick a random `k ∈ Zn`

``````E = gᵏ     (ephemeral key)
c = M·m    (encrypted message)
``````

The complete ciphertext is the tuple `(E, c)`.

Decryption

``````M = Eˣ     (recovery of M using secret key)
m = M⁻¹·c
``````

Because, by definition of generator, `gⁿ = 1` then `∀ x ∈ Z, gⁿ⁻ˣ·gˣ = 1` which implies `g⁻ˣ = gⁿ⁻ˣ`. For example if `G = Zp*`: `n = p-1` and thus `g⁻ˣ ≡ gᵖ⁻¹⁻ˣ (mod p)`.

ElGamal is a probabilistic encryption scheme, which means that given the random parameter `k` the ciphertext is random as well.

#### Malleability

Given the ciphertext `(E, c = M·m)` the corresponding plaintext can be predictably multiplied by a factor `z` by multiplying `c` by the same factor `z`.

``````c' = z·c  →  m' = M⁻¹·c' = M⁻¹·z·c = M⁻¹·z·M·m = z·m
``````

### Signature

Given the message `m`, the secret key `x` and public key `y = gˣ`, pick a random scalar `k` which is relatively prime with the group order `n`.

``````R = gᵏ
r = map_to_group_ord(R)
s = (m - x·r)·k⁻¹ mod n
``````

If `s = 0` we repeat with a different `k`.

The signature is the couple `(R, s)`.

Verification

Check if

`````` Rˢ·yʳ = gᵐ
``````

Proof

``````Rˢ = gᵏˢ = g^[k·(m - x·r)·k⁻¹] = g^(m - x·r)
yʳ = gˣʳ
→ Rˢ·bʳ = g^(m - x·r + x·r) = gᵐ
``````

#### Security

The verifier can’t disclose the secret `x` as he first needs to recover `k`, which imply finding the discrete logarithm for `r`.

The signer can’t forge signatures without knowing the secret `x`.

#### Existential forgery

1. One-parameter forgery. Select `e ∈ Zn`. Set `R = gᵉ·b` and `s = -r`. Then the tuple `(r, s)` is a valid signature for the message `m = e·s`

2. Two-parameters forgery. Select `e ∈ Zn` and `v ∈ Zn*`. Set `R = gᵉ·bᵛ` and `s = -r·v⁻¹`. Then the tuple `(r, s)` is a valid signature for the message `m = e·s`.

One-parameter forgery is a special case with `v = 1`.

This vulnerability is addressed by replacing `m` with `H(m)` in the signature and verification procedures. With `H` a cryptographic hash function.

#### Reusing random secret

If the same value `k` used to sign different messages then the secret key `x` can be easily recovered.

``````s₁ = (m₁ - x·r)·k⁻¹ mod n
s₂ = (m₂ - x·r)·k⁻¹ mod n
s₁ - s₂ = (m₁ - m₂)·k⁻¹ mod n
→  k = (m₁ - m₂)·(s₁ - s₂)⁻¹ mod n
→  x = (m₁ - s₁·k)·r⁻¹ mod n
``````

The only requirement is that `s₁ - s₂` is coprime with group order `n`.

## Digital Signature Standard

Also known as DSA (Digital Signature Algorithm), is a slightly modified version of ElGamal signature to address some of its weakness.

Parameters:

• `G`: cyclic group with prime order `n`
• `g ∈ G`: a generator for `G`
• `x ∈ Zn*`: secret key
• `y ∈ G`: public key such that `y = gˣ`
• `H`: a cryptographic hash function with `N` bits output and such that `n < 2ᴺ`.

Given a message `m`, we pick a random scalar `k ∈ Zn*`.

``````R = gᵏ
r = map_to_group_ord(R)
s = (H(m) + x·r)·k⁻¹ mod n
``````

If `s = 0` we repeat with a different `k`.

The signature is the couple `(r, s)`.

Verification

``````u = H(m)·s⁻¹ mod n
w = r·s⁻¹ mod n

V = gᵘ·yʷ
v = map_to_group_ord(V)

Valid if v = r
``````

Proof

``````gᵘ·yʷ = g^(u + x·w)

u + x·w ≡ H(m)·s⁻¹ + x·r·s⁻¹ ≡ s⁻¹·(H(m) + x·r) ≡ s⁻¹·s·k ≡ k (mod n)

→ gᵘ·bʷ = gᵏ
``````

Note that DSA is also more efficient than ElGamal signatures:

• DSA uses smaller exponents that ElGamal and still provide the same security. It works with a group with prime order which in general provides the same security as one bigger group with non-prime order (as for ElGamal).
• DSA signatures are shorter as both `r` and `s` are in `Zn`. In ElGamal we send the full `R ∈ G` as we need it for verification.
• On verification, only two exponentiation in `G` are performed, three with ElGamal.

### Reusing random secret

If the same value `k` used to sign different messages then the secret key `x` can be easily recovered.

``````s₁ = (H(m₁) + x·r)·k⁻¹ mod n
s₂ = (H(m₂) + x·r)·k⁻¹ mod n
s₁ - s₂ = (H(m₁) - H(m₂))·k⁻¹ mod n
→  k = (H(m₁) - H(m₂))·(s₁ - s₂)⁻¹ mod n
→  x = (s₁·k - m₁)·r⁻¹ mod n
``````

## Schnorr Scheme

### Interactive Schnorr Protocol

A kind of interactive zero-knowledge proof used to prove knowledge of some secret without revealing it.

In particular, in this context, it is used to prove the knowledge of the discrete logarithm of a value with respect to a public generator.

If `A` wants to prove to `B` the knowledge of the discrete logarithm `x` of some public value `y = gˣ`.

Parameters:

• `G`: cyclic group with prime order `n`
• `g ∈ G`: a generator for `G`
• `x ∈ Zn*`: secret scalar
• `y ∈ G`: public group element such that `y = gˣ`

Protocol:

1. Commitment: `A` chooses a random secret scalar `k`, computes `r = gᵏ`, and sends it to `B`.
2. Challenge: `B` chooses a random value `c` and sends it to `A`.
3. Proof: `A` computes `s = k + c·x mod n` and sends it to `B`.
4. Verification: `B` checks whether `gˢ = r·yᶜ`.

Verification Proof:

``````gˢ = g^(k + c·x) = gᵏ·gˣᶜ = r·yᶜ
``````

Security:

• To extract the secret `x`, `B` must compute `x = (s - k)·c⁻¹ mod n`. To do so, he must know the value of `k`, discrete log of `r`.
• `A` can’t cheat as well. The only way to cheat is if he’s able to know the value of `c` before committing the value `k`. In that case he can compute `r = gˢ·x⁻ᶜ` for an arbitrary value `s`.

### Non-Interactive Schnorr Protocol

The protocol can be made non-interactive by modifying the challenge step.

In this case the challenge value is obtained from a technique known as Fiat-Shamir Heuristic. In practice is computed as the output of a cryptographic hash function.

``````c = H(y || r)
``````

### Schnorr Signature

If we also bind a message `m` to the challenge then we obtain a Signature Scheme:

``````c = H(y || r || m)
``````

### Reusing random secret

If the same value `k` used with two different challenges then the secret key `x` can be easily recovered.

``````s₁ = k + c₁·x mod n
s₂ = k + c₂·x mod n
s₁ - s₂ = (c₁ - c₂)·x
→  x = (s₁ - s₂)·(c₁ - c₂)⁻¹ mod n
``````

## Chaum-Pedersen DLEQ Scheme

`P` wants to prove to `V` that two public values `y₁ = gˣ` and `y₂ = hˣ` have the same discrete logarithm with respect to the two generators `g` and `h`.

Parameters:

• `G₁` and `G₂`: two groups with same prime order `n`
• `g ∈ G₁` and `h ∈ G₂`: generators of `G₁` and `G₂` respectively
• `x ∈ Zn*`: secret scalar
• `y₁, y₂ ∈ G`: public group element such that `y = gˣ`

Protocol:

• Commitment: `P` chooses a random secret scalar `k` and sends to `V` the couple `r₁ = gᵏ` and `r₂ = hᵏ`.
• Challenge: `V` chooses a random scalar `c` and sends it to `P`.
• Proof: `P` computes `s = k - c·x mod n` and sends it to `V`.
• Verification: `V` checks if `r₁ = gˢ·y₁ᶜ` and `r₂ = hˢ·y₂ᶜ mod p`.

Note that the verification for the individual values is equal to the Schnorr protocol, as a consequence `P` also prove knowledge of the secret and not just equality.

The verification and security proofs are quite similar to the Schnorr protocol.

### Non-Interactive Chaum-Pedersen DLEQ Protocol

In this protocol the challenge `c` is computed as:

``````c = H(y₁ || y₂ || r₁ || r₂).
``````

## Diffie-Hellman Key Exchange Protocol

The protocol is used to securely generate a shared secret between two parties `A` and `B`.

Parameters:

• `G`: cyclic group with order `n`
• `g ∈ G`: a generator for `G`
• `a ∈ Zn*`: `A` secret key
• `yₐ ∈ G`: `A`’s public key `yₐ = gᵃ`
• `b ∈ Zn*`: `B` secret key
• `yᵦ ∈ G`: `B`’s public key `yᵦ = gᵇ`

Protocol:

• `A` generates a secret `a ∈ Zn*` and sends to `B` the public `yₐ = gᵃ`
• `B` generates a secret `b ∈ Zn*` and sends to `A` the public `yᵦ = gᵇ`
• `A` computes `k = yᵦᵃ`
• `B` computes `k = yₐᵇ`

The proof that the two parties gets the same `k` trivially follow the commutativity of the exponent in exponentiation.

### Man in The Middle Attack

In a public network there can be a third actor `C` that performs a DH protocol instance with both `A` and `B`. When communicates with `A` he impersonates `B` and when it communicates with `B` he impersonates `A`.

The popular defense is to introduce some form of data-origin authentication. For example by signing the ephemeral public keys with a key trusted by both the entities (i.e. some form of authority as done by PKI).

## Attacks to DLP

Attacks against DLP can be divided in two classes:

• generic: only use group operation, they work in any cyclic group;
• specialized: exploit special properties of a particular group.

Attacks can be further divided in two more classes:

• running time dependent on the size of the cyclic group;
• running time dependent on the size of the prime factors of the group order.

In the attacks’ analysis each step corresponds to a group operation.

Given the group `G` with order `n` and a group generator `g`, let’s assume we want to compute the discrete logarithm of `y = gˣ`.

Generic algorithm where we simply repeat the group operation for the generator `g` until the result is equal to `y`.

On average, for a random value `x`, we expect to find the correct solution after checking half of all the possibilities.

This gives a complexity of `O(n)` steps.

To make brute-force infeasible is thus sufficient to choose a group `G` with a sufficiently large order.

### Shanks’ Algorithm

Also known as Baby-Step Giant-Step method, is a generic algorithm which trades time for memory.

The discrete logarithm is rewritten as:

``````m = ⌈√n⌉
x = m·x₁ + x₂  , for 0 ≤ x₁, x₂ < m
``````

We rewrite `y` as:

``````y = gˣ = g^(m·x₁ + x₂) = g^(m·x₁) · g^x₂

→ y·g^(-m·x₁) = g^x₂
``````

The value of `g⁻ᵐ` is known. The algorithm tries to find the solution `(x₁, x₂)`.

The idea is to search for `x₁` and `x₂` separately.

In the first phase all the possible values for `g^x₂` are computed and stored.

This phase requires `O(√n)` steps and needs to store `O(√n)` group elements.

The computed values for `g^x₂` can be computed offline once (per group generator) and are independent on the exact value of `y`.

In the second phase we check for all `x₁` until we don’t find the value which satisfies the equation (using the pre-computed `x₂` values).

``````y·g^(-m·x₁) ≟ g^x₂
``````

The second phase requires `O(√n)` computational steps.

The implication of this attack is a reduction of complexity for the general DLP. For example, to achieve at least `128` bits of security we require `n ≥ 2^256`.

### Pollard’s Rho Algorithm

Probabilistic algorithm based on the birthday paradox, which asserts that to have a probability `p` of finding a collision by extracting elements from a uniform random distribution we need to extract

``````n(p) = ≈ √(2·n·ln(1/(1-p)))
``````

Thus, for example, with p = 0.5 we have n = √(2·ln(2)·n)

Pseudo-randomly generate group elements of the form `gⁱ·yʲ`.

For every element keep track of the values `i` and `j`.

Continue until we don’t find a collision:

``````g^i₁·y^j₁ = g^i₂·y^j₂
``````

``````i₁ + x·j₁ ≡ i₂ + x·j₂ (mod n)
i₁ - i₂ ≡ x·(j₂ - j₁) (mod n)
``````

If `gcd(j₂ - j₁, n) = 1`, then:

``````x = (i₁ - i₂)·(j₂ - j₁)⁻¹ mod n
``````

This algorithm is currently the best known algorithm for computing the discrete logarithm for elliptic curve groups.

A clever pseudo-random function for `i` and `j` generation is presented by Stinson1.

### Pohlig-Hellman Algorithm

Method based on the CRT exploiting the factorization of the group order `n = ∏ pᵢ^eᵢ`.

The algorithm tries to compute the smaller discrete logarithms `xᵢ = x mod pᵢ^eᵢ`.

Given `y = gˣ`, let `p` be a prime such that `pᵉ` is a factor of `n`. We want to compute the value of `r = xᵢ = x mod pᵉ` (without knowing `x` obviously).

Because `r < pᵉ`, then we can express `r` in radix `p` as:

``````r = ∑ rⱼ·pʲ , with 0 ≤ rⱼ < p  for 0 ≤ j < e
``````

Also, because `r = x mod pᵉ`, we can express `x` as:

``````x = s·pᵉ + r = s·pᵉ + ∑ rⱼ·pʲ
``````

For some integer `s`.

The first step is to compute `r₀` by observing that `y^(n/p) = g^(r₀·n/p)`. Proof:

``````y^(n/p) = g^(x·n/p)

→  x·n/p = (s·pᵉ + ∑ rⱼ·pʲ)·n/p = (K·p + r₀)·n/p
= K·n + r₀·n/p
≡ r₀·n/p (mod n)
``````

Using this fact we proceed by trying to find the `r₀` which satisfies the equation in `O(p)` steps.

If `e = 1` then we’re done. Otherwise, we proceed determining `rⱼ` for all the other `j < e`.

Define `y₀ = y` and `yⱼ = y·g^[-(r₀ + r₁·p + .. + rⱼ₋₁·pʲ⁻¹)]`.

This time we’ll use the generalized equation `yⱼ^(n/pʲ⁺¹) = g^(rⱼ·n/p)`. Proof:

``````yⱼ^(n/pʲ⁺¹) = g^[(x - r₀ - r₁·p - .. - rⱼ₋₁·pʲ⁻¹)·n/pʲ⁺¹]

→ (x - r₀ - r₁·p - .. - rⱼ₋₁·pʲ⁻¹)·n/pʲ⁺¹
= (rⱼ·pʲ + Kⱼ·pʲ⁺¹)·n/pʲ⁺¹
= rⱼ·pʲ·n/pʲ⁺¹ + Kⱼ·n
≡ rⱼ·n/p (mod n)
``````

Using this fact we proceed computing each `rⱼ` in `O(p)`.

Summarizing, each `r = xᵢ = x mod pᵉ` can be computed in `O(p)`.

This can be improved by noting that finding the solution `i` for `σ = g^(i·n/p)` is equivalent to find `i = log_[g^(n/p)](σ)`. The element `g^(n/p)` has order `p` therefore each element `i` can be computed using any other method we’ve already seen.

Once the values`xᵢ = pᵢ^eᵢ` for all the prime factors `pᵢ` are found, the solution for `n` is trivially found by direct application of CRT.

To contrast this attack the group order must have its largest prime factor in the range of `2^160`. In practice, often the group in which some schemes are defined has prime order.

### Index Calculus Algorithm

Efficient method for cyclic groups `Zp*` and `GF(2ᵐ)`.

The idea comes from the fact that a significant number of elements of `G` can be expressed as the product of elements of a small subset of `G` (e.g. for `Zp*` many elements can be expressed as the product of small primes).

The attack is so powerful that to provide 80-bit security the prime of a DLP in `Zp*` should be at least 1024 bit long.

#### Pre-Computation

Let `B = { pᵢ }` be a subset of (small) primes in `Zp*`.

In the first phase we find the logarithm of the `|B|` primes in `g` base.

Let `C` be the set of congruences defined using pseudo random values `xⱼ` and such that `g^xⱼ` has all its factors in `B` (we can use trial division):

``````C = { g^xⱼ ≡ ∏ pᵢ^aᵢⱼ (mod p) },  for some exponents set {aᵢⱼ}
``````

Define `|C|` to be slightly bigger that `|B|`.

The set `C` elements can be rewritten as:

``````xⱼ ≡ ∑ aᵢⱼ·log_g(pᵢ) (mod p-1)
``````

We end up with `|C|` congruences in `|B|` unknowns (`{log_g(pᵢ)}`) which hopefully have a unique solution modulo `p-1`.

This phase is carried out “offline” and an attacked can generate a big set of tuples `L = { log_g(pᵢ) }` for a generator `g`.

#### Attack

We want to recover the discrete logarithm for a generic `y = gˣ`.

Choose a random integer `s` (`0 < s < p - 1`) such that `σ = y·gˢ mod p` can be factored using just elements in `B`:

``````y·gˢ ≡ ∏ pᵢ^zᵢ (mod p)
``````

Which can be rewritten as:

``````log_g(y) + s ≡ ∑ zᵢ·log_g(pᵢ) (mod p-1)
``````

The only unknown in this equation is `log_g(y)`, which gives us `x`.

The asymptotic running times:

• pre-computation: `e^[(1 + o(1))·√(ln(p)·ln(ln(p)))]`
• attack: `e^[(1/2 + o(1))·√(ln(p)·ln(ln(p)))]`
• Cyclic groups notes
• Shanks algorithm Rust PoC here
• Reusing the ephemeral secret failure PoC here
• Merlin - Rust implementation which automates the Fiat-Shamir transform.

1. Cryptography Theory and Practice - Douglas Stinson ↩︎