#finite-fields #field #element #operations #ec #bitcoin #module

bitcoinsecp256k1-field

field element module for usage in EC

4 releases

0.1.16-alpha.0 Mar 31, 2023
0.1.13-alpha.0 Mar 17, 2023
0.1.12-alpha.0 Jan 19, 2023
0.1.10-alpha.0 Jan 18, 2023

#7 in #ec

Download history 177/week @ 2024-07-19 126/week @ 2024-07-26 134/week @ 2024-08-02 153/week @ 2024-08-09 121/week @ 2024-08-16 114/week @ 2024-08-23 124/week @ 2024-08-30 118/week @ 2024-09-06 123/week @ 2024-09-13 152/week @ 2024-09-20 89/week @ 2024-09-27 32/week @ 2024-10-04 88/week @ 2024-10-11 76/week @ 2024-10-18 63/week @ 2024-10-25 121/week @ 2024-11-01

348 downloads per month
Used in 92 crates (4 directly)

MIT license

590KB
2K SLoC

Rust 1.5K SLoC // 0.8% comments GNU Style Assembly 742 SLoC // 0.1% comments

Short Description Tag: Rust crate for working with finite fields in the secp256k1 elliptic curve cryptography.

Longer Description:

The bitcoinsecp256k1-field Rust crate is a component of the bitcoin system that provides utilities for working with finite fields in the secp256k1 elliptic curve cryptography. This crate provides a set of operations for manipulating finite fields that are used in secp256k1 curve arithmetic. The finite field arithmetic operations include addition, multiplication, inversion, and square root of field elements.

This crate also provides utilities for serialization and deserialization of field elements from and to binary, as well as comparison functions and normalization functions for field elements.

The underlying mathematical concepts used in this crate include arithmetic operations on finite fields, which are essential for elliptic curve cryptography. The secp256k1 curve is defined over the finite field of integers modulo a prime, and this crate provides the necessary operations for manipulating elements in this field.

Note that this crate is part of a direct translation from C++ to Rust of the bitcoin core. As such, some of the function bodies may still be in the process of translation. Please note that this system will become testable once the translation process is complete.

135> what is a finite field?

In abstract algebra, a finite field (also called Galois field) is a field that contains a finite number of elements. Fields are algebraic structures that generalize the properties of arithmetic operations like addition, subtraction, multiplication, and division, and they are important in various areas of mathematics and computer science, including cryptography, coding theory, and number theory.

A finite field is specified by a prime number p and a positive integer n, and denoted as GF(p^n). The field contains p^n elements, which can be thought of as the numbers from 0 to p^n-1. The arithmetic operations in the field are defined such that the result is always another element of the field, and they satisfy certain properties such as commutativity, associativity, distributivity, and the existence of additive and multiplicative inverses.

One important property of finite fields is that the arithmetic operations are performed modulo p, where p is a prime number. This means that the values wrap around when they reach p, so the result is always within the range 0 to p-1. Another important property is that the elements of a finite field form a cyclic group under multiplication, which means that every non-zero element has a multiplicative inverse.

Finite fields have many applications in cryptography, including the encryption and decryption of data, the generation and verification of digital signatures, and the implementation of cryptographic protocols such as Diffie-Hellman key exchange and elliptic curve cryptography.

136> what are the properties of a finite field?

A finite field, also known as a Galois field, is a field with a finite number of elements. The properties of a finite field include:

  1. Finite: The field has a finite number of elements, usually denoted by q.

  2. Commutative: Addition and multiplication are commutative, meaning a + b = b + a and ab = ba for all a, b in the field.

  3. Associative: Addition and multiplication are associative, meaning (a + b) + c = a + (b + c) and (ab)c = a(bc) for all a, b, c in the field.

  4. Distributive: Multiplication is distributive over addition, meaning a(b + c) = ab + ac for all a, b, c in the field.

  5. Existence of identity elements: The field has an additive identity element 0 and a multiplicative identity element 1 such that a + 0 = a and a1 = a for all a in the field.

  6. Existence of inverse elements: Every nonzero element a in the field has an additive inverse -a and a multiplicative inverse a^-1 such that a + (-a) = 0 and aa^-1 = 1.

  7. Unique factorization: Every nonzero element a in the field can be written as a product of irreducible elements, meaning elements that cannot be factored further into a product of nonzero elements.

  8. Characteristic: The field has a characteristic p, which is the smallest positive integer such that pa = 0 for all a in the field. If no such integer exists, the characteristic is 0.

137> why are they useful in cryptography?

Finite fields are useful in cryptography because they provide a mathematical structure that allows for efficient arithmetic operations, which are required for many cryptographic algorithms. In particular, elliptic curve cryptography, which is widely used in modern cryptography for key exchange, digital signatures, and encryption, relies heavily on finite fields.

Finite fields are also used in symmetric-key cryptography, where they are used to perform operations such as key expansion and permutation. Additionally, they are used in error-correcting codes, which are used to detect and correct errors in data transmission, such as in satellite communication and in storage devices.

Overall, finite fields provide a powerful mathematical tool for cryptography, enabling the development of efficient and secure cryptographic algorithms.

138> how do we represent them in computer memory?

Finite fields can be represented in computer memory using various methods. One common way is to represent each element of the field as an integer modulo a prime number, where the prime number is chosen to be sufficiently large to accommodate the desired field size.

For example, in the case of the finite field GF(2^256), the elements of the field can be represented as integers modulo the prime number 2^256 - 2^32 - 2^9 - 2^8 - 2^7 - 2^6 - 2^4

    1. This means that every element in the field can be represented as an unsigned 256-bit integer, where the value of the integer is reduced modulo the prime number to obtain the corresponding field element.

Another way to represent finite fields in computer memory is to use polynomial arithmetic, where each element of the field is represented as a polynomial of degree less than the degree of an irreducible polynomial chosen to define the field. For example, in the case of GF(2^256), the field can be defined using the irreducible polynomial x^256 + x^10 + x^5 + x^2 + 1, and each element of the field can be represented as a polynomial of degree less than 256 with coefficients in the binary field GF(2). Polynomial arithmetic provides a more compact representation of field elements, but may be less efficient than integer arithmetic for some operations.

139> what should we watch out for when working with finite fields?

When working with finite fields, there are several things to keep in mind:

  1. Modular arithmetic: All operations in a finite field are performed modulo some prime number or irreducible polynomial. It is important to ensure that all intermediate values and results are also reduced modulo this prime or polynomial to prevent overflow and ensure that calculations are consistent with the field properties.

  2. Choice of prime or polynomial: The choice of prime or irreducible polynomial used to define the finite field can have a significant impact on the security and performance of cryptographic algorithms that use the field. It is important to choose a prime or polynomial that is sufficiently large and randomly chosen to prevent attacks such as discrete logarithm or polynomial factorization.

  3. Timing attacks: Timing attacks can be used to gain information about private keys by measuring the time taken to perform certain operations in a finite field. Implementations should use constant-time algorithms to prevent such attacks.

  4. Side-channel attacks: Other side-channel attacks such as power analysis or electromagnetic analysis can be used to extract information about private keys by measuring the power consumption or electromagnetic emissions of devices performing cryptographic operations. Countermeasures such as shielding, power filtering, and secure programming techniques can be used to prevent these attacks.

  5. Implementation errors: Errors in the implementation of cryptographic algorithms using finite fields can lead to vulnerabilities such as buffer overflows, integer overflows, and memory corruption. Implementations should be thoroughly tested and verified to ensure correctness and security.

Dependencies

~87MB
~832K SLoC