Modular arithmetic

Modular arithmetic is used for many public key cryptosystems, including public-key encryption algorithms like RSA and key exchange protocols like Diffie-Hellman.

public-key encryption: Encryption using a pair of distinct keys for encryption and decryption. Also known as asymmetric-key encryption.

Modular arithmetic is something most people actually already understand, they just don’t know it’s called that. We can illustrate the principles of modular arithmetic using a clock.


For simplicity’s sake, our demonstration 12-hour clock only shows hours, not minutes or seconds. Also unlike real clocks, the hour hand is never halfway in between two hours: it always shows an exact hour, such as 2 or 9.

Addition and subtraction

It obviously makes sense to add hours on our clock: if it’s 2 o’clock now, and you’d like to know what time it is five hours from now, you can add 5, and end up with 7.


Similarly, we can subtract times. If it’s 10 o’clock now, and you’d like to know what time it was two hours ago, you subtract 2 and end up with 8.


The “weird” part is when you cross the boundary at 12. As far as the clock is concerned, there’s no real difference between 12 and 0. If it’s 10 o’clock now, it’ll be 2 o’clock in four hours. If it’s 2 o’clock now, it was 9 o’clock five hours ago.

This is an example of what’s called “modular arithmetic”. The modulus, in this case, is 12. We can write the above equations as:

\[(10 + 4) \bmod{12} = 2\] \[(2 - 5) \bmod{12} = 9\]

In these equations, the \(\bmod\) is an operator, giving the remainder after division. When we are dealing with modular arithmetic, where all operations are affected by the modulus instead of a simple single operation, we’ll instead write \(\negthickspace\pmod{12}\) at the end of the equation and use an \(\equiv\) sign instead of an equals sign (=):

\[10 + 4 \equiv 2 \pmod{12}\] \[2 - 5 \equiv 9 \pmod{12}\]

This is read as “ten plus four is equivalent to two, modulo twelve” and “two minus five is equivalent to nine, modulo twelve”. That might seem like a trivial notational hack now, but the difference will become apparent once we start applying tricks for doing more complex modular computations, like multiplication and exponentiation.

In general, we call two numbers equivalent modulo some modulus if dividing them by the modulus leaves the same remainder. We can illustrate this with our previous examples: 10 + 4 = 14 leaves a remainder of 2 when divided by 12, so it is equivalent to 2 modulo 12. For negative numbers, we’ll always use positive remainders. For example, \(2 - 5 \equiv 9 \pmod{12}\). This is exactly the way a clock works as well: if it’s 2 o’clock now, then five hours ago was “nine o’clock”, not “minus three o’clock”.

Prime numbers

Prime numbers are wonderful kinds of numbers that come back in many branches of mathematics. Anything I say about them probably won’t do them justice; but we’re in a practical book about applied cryptography, so we’ll only see a few properties.

A prime number is a number that is divisible only by two numbers: 1 and itself. For example, 3 is a prime number, but 4 is not, because it can be divided by 2.

Any number can be written as a product of prime factors: a bunch of prime numbers multiplied together. That product is called a prime factorization. For example, 30 can be factorized into 2, 3 and 5:

\[30 = 2 \cdot 3 \cdot 5\]

Sometimes, a prime number will occur more than once in a factorization. For example, the factorization of 360 has 2 in it three times, and three in it twice:

\[360 = 2^3 \cdot 3^2 \cdot 5\]

The factorization of any prime number is just that prime number itself.

Modern mathematics no longer considers 1 to be a prime number, even though it is only divisible by 1 and itself (1 again). Under this convention, every number not only has a factorization, but that factorization is unique. Otherwise, 4 could be factored not only as \(2 \cdot 2\), but also as \(2 \cdot 2 \cdot 1\), \(2 \cdot 2 \cdot 1 \cdot 1\), and so on. The uniqueness of factorization helps in some important proofs in number theory.

Also, 0 is not a prime number, as it is divisible by many numbers: all numbers except 0 itself.

Two numbers are called coprime when their greatest common divisor is 1, or, to put it in another way, they don’t share any prime factors. Since the only prime factor a prime has is itself, that means that all prime numbers are also coprime. More generally, a prime is coprime to any number that isn’t a multiple of that prime.


You might remember you were first taught multiplication as repeated addition:

\[n \cdot x = \underbrace{x + x + \ldots + x}_{n \text{ times}}\]

Modular multiplication is no different. You can compute modular multiplication by adding the numbers together, and taking the modulus whenever the sum gets larger than the modulus. You can also just do regular multiplication, and then take the modulus at the end.

Division and modular inverses

Division is defined as the inverse of multiplication. So, \(a \cdot b \equiv c \pmod m\), then \(\frac{c}{b} \equiv a \pmod m\).

For example, \(5 \cdot 6 \equiv 2 \pmod 7\); so: \(\frac{2}{6} \equiv 5 \pmod 7\). This is because \(5 \cdot 6 = 30\), which leaves a remainder of 2 when divided by 7.

Usually, instead of using division directly, we’ll multiply using something called a modular inverse. The modular inverse of a is a number, that when you multiply it with a, you get 1. This is just like the inverse of a number in regular arithmetic: \(x \cdot \frac{1}{x} = 1\).

Like in regular arithmetic, not all numbers have modular inverses. This is the equivalent of dividing by zero in regular arithmetic.

There are two algorithms that are used to compute modular inverses: the extended Euclidean algorithm, and with the help of Euler’s theorem.

The extended Euclidean algorithm

The extended Euclidean algorithm is an extension to the Euclidean algorithm (which gives us an efficient method for computing the greatest common divisor of two integers). The extended algorithm additionally helps us express this GCD of two integers as a linear combination of the two original numbers:

\[GCD(x, y) = Px + Qy\]

Given the numbers x and y, the extended Euclidean algorithms gives us a way to efficiently calculate the value of coefficients P and Q.

This algorithm can be further used to calculate multiplicative inverses because of the known property that the inverse of number \(a \pmod n\) can only exist if gcd\((a, n) = 1\). Therefore we can substitute x & y with a & n respectively in the extended Euclidean algorithm equation:

\[1 = Pa + Qn\]

Writing this equation in \(\pmod n\) will lead to elimination of the \(Qn\) factor because any multiple of n is congruent to \(0 \pmod n\).

\[1 \equiv Pa \pmod n\] \[a^{-1} \equiv P \pmod n\]

This proves that the coeffiecient \(P \pmod n\) is the multiplicative inverse of a, and we can use the extended Euclidean algorithm to calculate the value of coefficient P.

Using Euler’s theorem

Euler’s theorem states that if two numbers a and n are coprime, then:

\[a^{\phi(n)} \equiv 1 \pmod n\]

In that equation, \(\phi\) is Euler’s totient function, which counts the amount of numbers that are coprime to (and less than or equal to) its argument. As an example, the totient of 10 is 4, as 1, 3, 7, and 9 do not have common prime factors with 10.

We can use Euler’s theorem to find the multiplicative inverse of a. If we just multiply both sides of the equation by \(a^{-1}\), we get:

\[a^{\phi(n) - 1} \equiv a^{-1} \pmod n\]

That gives us a direct formula for computing \(a^{-1}\). Unfortunately, this is still generally less interesting than using the extended Euclidean algorithm, for two reasons:

  1. It requires computing the totient function, which is harder than running the extended Euclidean algorithm in the first place, unless you happen to know the prime factors of n.
  2. Modular exponentiation is computationally expensive.

One exception to that rule is for prime moduli. Since a prime is coprime to every other number, and since there are \(p - 1\) numbers smaller than p, \(\phi(p) = p - 1\). So, for a prime modulus, the modular inverse of a is simply:

\[a^{-1} \equiv a^{\phi(p) - 1} \equiv a^{p - 2} \pmod p\]

This still requires us to be able to efficiently raise a to a power using modular arithmetic. We’ll discuss how you can do that efficiently in the next section.


Like multiplication is taught as repeated addition, exponentiation can be thought of as repeated multiplication:

\[a^n = \underbrace{a \cdot a \cdot \ldots \cdot a}_{n \text{ times}}\]

As with multiplication, it’s possible to compute modular exponentiation by performing regular exponentiation, and then taking the modulus at the end. However, this is very inefficient, particularly for large n: the product quickly becomes far too large.

Fortunately, it is possible to compute modular exponentiation much more efficiently. This is done by splitting the problem up into smaller sub-problems. For example, instead of computing \(2^{20}\) directly you could split it up:

\[2^{20} = (2^{10})^2\]

\(2^{10}\) is something you can compute on your hands: start at 2, which is \(2^1\), and then keep multiplying by two. Every time you multiply by two, the exponent goes up by 1, so by the time you’ve counted all your fingers (assuming you have ten of them), you’re done. The result is

  1. So:
\[\begin{aligned} \begin{aligned} 2^{20} &\equiv (2^{10} \bmod {15})^2 \pmod {15} \\ &\equiv (1024 \bmod {15})^2 \pmod {15} \\ &\equiv 4^2 \pmod {15} \\ &\equiv 16 \pmod {15} \\ &\equiv 1 \pmod {15} \end{aligned} \end{aligned}\]

Exponentiation by squaring

A particularly efficient way to do it on computers is splitting the exponent up into a sum of powers of two. This is called exponentiation by squaring, or sometimes also binary exponentiation. Suppose we want to compute \(3^{209} \pmod {19}\). First, we split up 209 into a sum of powers of two. This process is essentially just writing 209 down in binary: 0b11010001. That’s very practical if the computation is being performed by a computer, because that’s typically how the computer had the number stored in the first place.

\[\begin{aligned} \begin{array}{lllllllll} 209 &= 1 \cdot 2^{7} &+ 1 \cdot 2^{6} &+ 0 \cdot 2^{5} &+ 1 \cdot 2^{4} &+ 0 \cdot 2^{3} &+ 0 \cdot 2^{2} &+ 0 \cdot 2^{1} &+ 1 \cdot 2^{0} \\ &= 1 \cdot 128 &+ 1 \cdot 64 &+ 0 \cdot 32 &+ 1 \cdot 16 &+ 0 \cdot 8 &+ 0 \cdot 4 &+ 0 \cdot 2 &+ 1 \cdot 1 \\ &= 128 &+ 64 & &+ 16 & & & &+ 1 \end{array} \end{aligned}\]

We use that expansion into a sum of powers of two to rewrite the equation:

\[\begin{aligned} \begin{aligned} 3^{209} &= 3^{128 + 64 + 16 + 1} \\ &= 3^{128} \cdot 3^{64} \cdot 3^{16} \cdot 3^1 \end{aligned} \end{aligned}\]

Now, we need to compute those individual powers of 3: 1, 16, 64 and 128. A nice property of this algorithm is that we don’t actually have to compute the big powers separately from scratch. We can use previously computed smaller powers to compute the larger ones. For example, we need both \(3^{128} \pmod {19}\) and \(3^{64} \pmod {19}\), but you can write the former in terms of the latter:

\[3^{128} \bmod {19} = (3^{64} \bmod {19})^2 \pmod {19}\]

Let’s compute all the powers of 3 we need. For sake of brevity, we won’t write these out entirely, but remember that all tricks we’ve already seen to compute these still apply:

\[\begin{aligned} \begin{aligned} 3^{16} &\equiv 17 \pmod {19} \\ 3^{64} &\equiv (3^{16})^4 \equiv 17^4 \equiv 16 \pmod {19} \\ 3^{128} &\equiv (3^{64})^2 \equiv 16^2 \equiv 9 \pmod {19} \end{aligned} \end{aligned}\]

Filling these back in to our old equation:

\[\begin{aligned} \begin{aligned} 3^{209} &= 3^{128} \cdot 3^{64} \cdot 3^{16} \cdot 3^1 \pmod {19} \\ &\equiv 9 \cdot 16 \cdot 17 \cdot 3 \pmod {19} \end{aligned} \end{aligned}\]

This trick is particularly interesting when the exponent is a very large number. That is the case in many cryptographic applications. For example, in RSA decryption, the exponent is the private key d, which is usually more than a thousand bits long. Keep in mind that this method will still leak timing information, so it’s only suitable for offline computation. Modular exponentiation can also be computed using a technique called a Montgomery ladder, which we’ll see in the next section.

Many programming languages provide access to specific modular exponentiation functions. For example, in Python, pow(e, x, m) performs efficient modular exponentiation. However, the expression (e ** x) % m will still use the inefficient method.

Montgomery ladder exponentiation

As we mentioned before, the exponentiation by squaring algorithm is simple and fast, but the time it takes to complete depends on the value of the exponent. That’s bad, because the exponent is usually a secret value, such as a Diffie-Hellman secret or the private exponent d in RSA.

The Montgomery ladder is an algorithm that resolves this by guaranteeing the same number of operations irrespective of the particular value of the exponent. It was originally applied for efficient scalar multiplications over elliptic curves, but the mathematics works for many other systems: specifically, for any abelian group.

Deriving the ladder

Like with exponentiation by squaring, we start by looking at the binary expansion of the exponent k. Generally, any k can be written as a sum (\(\sum\)) of some powers of two (\(2^i\)). If \(2^j\) appears in the binary expansion, we’ll say that \(k_j = 1\); if it doesn’t, we’ll say that \(k_j = 0\). That gives us:

\[k = \sum_{i=0}^{t-1} 2^i k_i\]

That definition might look scary, but all you’re really doing here is defining \(k_i\) as bit of k at position i. The sum goes over all the bits: if k is t bits long, and we start indexing at 0, the index of the highest bit is t - 1, and the index of the lowest bit is 0. For example, the binary expansion of the number 6 is 0b110. That number is three bits long, so t = 3. So:

\[\begin{aligned} \begin{aligned} 6 &= \sum_{i = 0}^{t - 1} 2^i k_i \\ &= \sum_{i = 0}^{2} 2^i k_i \\ &= k_2 \cdot 2^2 + k_1 \cdot 2^1 + k_0 \cdot 2^0 \\ &= 1 \cdot 2^2 + 1 \cdot 2^1 + 0 \cdot 2^0 \end{aligned} \end{aligned}\]

So, \((k_2, k_1, k_0) = (1, 1, 0)\).

The next few steps don’t make a lot of sense until you see them come together at the end, so bear with me and check that the math works out. We’ll define a related sum, \(L_j\):

\[L_j = \sum_{i = j}^{t - 1} 2^{i - j} k_i\]

For example, \(L_1\) (still with \(k = 6\)) becomes:

\[\begin{aligned} \begin{aligned} L_1 & = \sum_{i = 1}^{2} 2^{i - 1} k_i \\ & = \underbrace{2^1 \cdot k_2}_{i = 2} + \underbrace{2^0 \cdot k_1}_{i = 1} \\ & = 2 \cdot 1 + 1 \cdot 1 \\ & = 3 \end{aligned} \end{aligned}\]

Essentially, \(L_j\) is just k shifted to the right by j bits. Shifting to the right by one bit is the same thing as flooring division by two, just like right-shifting by a decimal digit is the same thing as flooring division by 10. For example: 73, shifted one decimal digit to the right is 7; 0b101 (5) shifted one binary digit (bit) to the right is 0b10 (2). Analogously, shifting left is the inverse operation, and is equivalent to multiplying by two.

Next, we’ll perform a little arithmetical hocus pocus. First of all:

\[L_j = 2 \cdot L_{j + 1} + k_j\]

While you can verify this arithmetically, the easiest way to check this is to think of it in terms of right and left shifts. If you shift k to the right by j positions, that

\[\begin{aligned} \begin{aligned} k & = \mathtt{0b110010111} \\ L_j = L_2 & = \mathtt{0b1100101} \\ L_{j + 1} = L_3 & = \mathtt{0b110010} \\ 2 \cdot L_{j + 1} = 2 \cdot L_3 & = \mathtt{0b1100100} \end{aligned} \end{aligned}\]

You can visually verify that \(L_2\) is indeed \(L_3\), shifted one to the left (which is the same thing as multiplying by two), plus that one bit \(k_j\) that “fell off” when shifting right. \(k_j\) is the last bit of \(L_j\); in this case it happens to be 1, but it could equally well have been 0.

We define another very simple function \(H_j\):

\[H_j = L_j + 1 \iff L_j = H_j - 1\]

Starting from our previous result:

\[\begin{aligned} \begin{aligned} L_j & = 2 \cdot L_{j + 1} + k_j \\ & \Downarrow (L_{j + 1} = H_{j+1} - 1) \\ L_j & = L_{j + 1} + k_j + H_{j + 1} - 1 \\ & \Downarrow (L_{j + 1} = H_{j+1} - 1) \\ L_j & = 2 \cdot H_{j + 1} + k_j - 2 \end{aligned} \end{aligned}\]

We can combine these to produce an inductive way to compute \(L_j\) and \(H_j\):

\[\begin{aligned} L_j = \begin{cases} 2 L_{j + 1} & \mbox{if } k_j = 0, \\ L_{j + 1} + H_{j + 1} & \mbox{if } k_j = 1. \end{cases} \end{aligned}\] \[\begin{aligned} H_j = \begin{cases} L_{j + 1} + H_{j + 1} & \mbox{if } k_j = 0, \\ 2 H_{j + 1} & \mbox{if } k_j = 1. \end{cases} \end{aligned}\]

Remember that we’re doing this to compute \(g^k\). Let’s write the exponentiation out:

\[\begin{aligned} g^{L_j} = \begin{cases} g^{2 L_{j + 1}} = \left(g^{L_{j + 1}}\right)^2 & \mbox{if } k_j = 0, \\ g^{L_{j + 1} + H_{j + 1}} = g^{L_{j + 1}} \cdot g^{H_{j+1}} & \mbox{if } k_j = 1. \end{cases} \end{aligned}\] \[\begin{aligned} g^{H_j} = \begin{cases} g^{L_{j + 1} + H_{j + 1}} = g^{L_{j + 1}} \cdot g^{H_{j+1}} & \mbox{if } k_j = 0, \\ g^{2 H_{j + 1}} = \left(g^{H_{j + 1}}\right)^2 & \mbox{if } k_j = 1. \end{cases} \end{aligned}\]

Remember that \(L_j\) is k right-shifted by j bits, so \(L_0\) is k shifted right by 0 bits, or just k itself. That means \(g^k\), the number we’re trying to compute, is the same thing as \(g^{L_0}\). By starting at \(g^{L_{t - 1}}\) (g raised to the power of the leftmost bit of k) and iteratively making our way down to \(g^{L_0} = g^k\), we have an elegant inductive method for computing \(g^k\) based on two simple recursive rules.

The important part about this algorithm is the constant number of operations. If \(k_j = 0\), computing \(g^{L_j}\) involves one squaring and \(g^{H_j}\) involves one multiplication; if \(k_j = 1\), it’s the other way around. No matter what any of the bits of k are, you need one squaring operation and one multiplication per bit.

Implementing the Montgomery ladder in Python

The Python implementation of this algorithm, applied to modular exponentiation, is surprisingly terse:

def montgomery(x, exponent, modulus):
    x1, x2 = x, x ** 2
    high_bit, *remaining_bits = bits(exponent)
    for bit in remaining_bits:
        if bit == 0:
            x2 = x1 * x2
            x1 = x1 ** 2
            x1 = x1 * x2
            x2 = x2 ** 2
        x1, x2 = x1 % modulus, x2 % modulus
    return x1

This code block doesn’t show the definition of bits: it produces the binary expansion of its argument. Python doesn’t provide that by default; bin is close, but that produces a string: bin(100) evaluates to 0b1100100. The a, *b = bits(...) construct assigns the first item in bits(...) to a, and all remaining bits to b, effectively just skipping the first bit.

The important thing to note here is that no matter what the particular value of the exponent is, there is one squaring, one multiplication, and one modulo operation per bit. Keep in mind that this doesn’t necessarily make the entire algorithm take constant time, because the individual squaring and multiplication operations are not necessarily constant time.

Discrete logarithm

Just like subtraction is the inverse of addition, and division is the inverse of multiplication, logarithms are the inverse of exponentiation. In regular arithmetic, \(b^x = y\), if \(x = \log_b y\). This is pronounced “b raised to the power x is y”, and “the logarithm of y with respect to b is x”. The equivalent of this in modular arithmetic is called a “discrete logarithm”.

As with division, if you start from the definition as the inverse of a different operator, it’s easy to come up with examples. For example, since \(3^6 \equiv 9 \pmod {15}\), we can define \(6 \equiv \log_3 9 \pmod {15}\). Unlike modular inverses, computing discrete logarithms is generally hard. There is no formal proof that computing discrete logarithms is intrinsically complex; we just haven’t found any efficient algorithms to do it. Because this field has gotten extensive research and we still don’t have very fast general algorithms, we consider it safe to base the security of protocols on the assumption that computing discrete logs is hard.

There is one theoretical algorithm for computing discrete logarithms efficiently. However, it requires a quantum computer, which is a fundamentally different kind of computer from the classical computers we use today. While we can build such computers, we can only build very small ones. The limited size of our quantum computers strongly limits which problems we can solve. So far, they’re much more in the realm of the kind of arithmetic a child can do in their head, than ousting the top of the line classical computers from the performance throne.

The complexity of computing discrete logarithms, together with the relative simplicity of computing its inverse, modular exponentiation, is the basis for many public key cryptosystems. Common examples include the RSA encryption primitive, and the Diffie-Hellman key exchange protocol.

While cryptosystems based on the discrete logarithm problem are currently considered secure with appropriate parameter choices, there are certainly ways that could change in the future. For example:

  • Theoretical breakthroughs in number theory could make discrete logarithms significantly easier to compute than we currently think.
  • Technological breakthroughs in quantum computing could lead to large enough quantum computers.
  • Technological breakthroughs in classical computing as well as the continuous gradual increases in performance and decreases in cost could increase the size of some problems that can be tackled using classical computers.

Discrete logarithm computation is tightly linked to the problem of number factorization. They are still areas of active mathematical research; the links between the two problems are still not thoroughly understood. That said, there are many similarities between the two:

  • Both are believed to be hard to compute on classical computers, but neither has a proof of that fact.
  • They can both be efficiently computed on quantum computers using Shor’s algorithm.
  • Mathematical advances in one are typically quickly turned into mathematical advances in the other.

Multiplicative order

Given integer a and positive integer b with gcd\((a, b) = 1\), the multiplicative order of \(a \pmod{b}\) is the smallest positive integer k such that \(a^k = 1 \pmod{b}\).

Elliptic curves

Like modular arithmetic, elliptic curve arithmetic is used for many public key cryptosystems. Many cryptosystems that traditionally work with modular arithmetic, such as Diffie-Hellman and DSA, have an elliptic curve counterpart.

Elliptic curves are curves with the following form:

\[y^2 = x^3 + ax + b\]

This is called the “short Weierstrass form”, and is the most common form when talking about elliptic curves in general. There are several other forms which mostly have applications in cryptography, notably the Edwards form:

\[x^2 + y^2 = 1 + dx^2y^2\]

We can define addition of points on the curve.

All of this put together form something called an Abelian group. That’s a scary-sounding mathematical term that almost everyone already understands the basics of. Specifically, if you know how to add integers (\(\ldots -2, -1, 0, 1, 2, \ldots\)) together, you already know an Abelian group. An Abelian group satisfies five properties:

  1. If a and b are members of the Abelian group and \(\star\) is the operator, then \(a \star b\) is also a member of that Abelian group. Indeed, any two integers added together always get you another integer. This property is called closure, or, we say that the group is closed under addition (or whatever the name is of the operation we’ve defined).
  2. If a, b and c are members of the Abelian group, the order of operations doesn’t matter; to put it differently: we can move the brackets around. In equation form: \((a \star b) \star c = a \star (b \star c)\). Indeed, the order in which you add integers together doesn’t matter; they will always sum up to the same value. This property is called associativity, and the group is said to be associative.
  3. There’s exactly one identity element i, for which \(a \star i = i \star a = a\). For integer addition, that’s zero: \(a + 0 = 0 + a = a\) for all a.
  4. For each element a, there’s exactly one inverse element b, for which \(a \star b = b \star a = i\), where i is the identity element. Indeed, for integer addition, \(a + (-a) = (-a) + a = 0\) for all a.
  5. The order of elements doesn’t matter for the result of the operation. For all elements \(a, b\), \(a \star b = b \star a\). This is known as commutativity, and the group is said to be commutative.

The first four properties are called group properties and make something a group; the last property is what makes a group Abelian.

We can see that our elliptic curve, with the point at infinity and the addition operator, forms an Abelian group:

  1. If P and Q are two points on the elliptic curve, then \(P + Q\) is also always a point on the curve.
  2. If P, Q, and R are all points on the curve, then \(P + (Q + R) = (P + Q) + R\), so the elliptic curve is associative.
  3. There’s an identity element, our point at infinity O. For all points on the curve P, P + O = O + P = P.
  4. Each element has an inverse element. This is easiest explained visually TODO: Explain visually
  5. The order of operations doesn’t matter, P + Q = Q + P for all P, Q on the curve.

The elliptic curve discrete log problem

As with the regular discrete log problem, the elliptic curve discrete log problem doesn’t actually have a formal proof that the operation is “hard” to perform: we just know that there is no publicly available algorithm to do it efficiently. It’s possible, however unlikely, that someone has a magical algorithm that makes the problem easy, and that would break elliptic curve cryptography completely. It’s far more likely that we will see a stream of continuous improvements, which coupled with increased computing power eventually eat away at the security of the algorithm.

Side-channel attacks

Timing attacks

AES cache timing

Copyright (C) CodeAhoy. This book is licensed under the “Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

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This work is a derivative of “Crypto 101”. Original license CC BY-NC 4.0.

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