Shamir's Secret Sharing

Shamir's Secret Sharing, formulated by Adi Shamir, is one of the first secret sharing schemes in cryptography. It is based on polynomial interpolation over finite fields.[1]

High-level explanationEdit

Shamir's Secret Sharing (SSS) is used to secure a secret in a distributed way, most often to secure other encryption keys. The secret is split into multiple parts, called shares, which individually should not give any information about the secret.

To unlock the secret via Shamir's secret sharing, a minimum number of shares are needed. This is called the threshold, and is used to denote the minimum number of shares needed to unlock the secret. An adversary who discovers any number of shares less than the threshold will not have any additional information about the secured secret-- this is called perfect secrecy. In this sense, SSS is a generalisation of the one-time pad (which can be viewed as SSS with a two-share threshold and two shares in total).

Consider an example:

Problem: Company XYZ needs to secure their vault's passcode. They could use something standard, such as AES, but the key holder could be unavailable or die. The key could be compromised by a malicious hacker or the holder of the key could turn rogue, and the adversary could use the key to their benefit.

SSS can be used in this situation. It can be used to share the vault's passcode and generate a certain number of shares, where a certain number of shares can be allocated to each executive within Company XYZ. Now, the executives can only unlock the vault if they combine more shares than the threshold. The threshold can be appropriately set for the number of executives, so the vault is always accessible by the authorized individuals. If a small number of shares were compromised, these shares could not be used to find the passcode unless other executives cooperated.

Mathematical formulationEdit

Shamir's Secret Sharing is an ideal and perfect  -threshold scheme. In such a scheme, the aim is to divide a secret   (for example, the combination to a safe) into   pieces of data   (known as shares) in such a way that:

  1. Knowledge of any   or more shares   makes   easily computable. That is, the complete secret   can be reconstructed from any combination of   shares of data.
  2. Knowledge of any   or fewer shares   leaves   completely undetermined, in the sense that the possible values for   seem as likely with knowledge of up to   shares as with knowledge of   shares. The secret   cannot be reconstructed with fewer than   shares.

If  , then every piece of the original secret   is required to reconstruct the secret.

One can draw an infinite number of polynomials of degree 2 through 2 points. 3 points are required to uniquely determine a polynomial of degree 2. This image is for illustration purposes only — Shamir's scheme uses polynomials over a finite field, which are not easy to represent in a 2-dimensional plane.

The essential idea of the scheme is based on the Lagrange interpolation theorem, specifically that   points is enough to uniquely determine a polynomial of degree less than or equal to  . For instance, 2 points are sufficient to define a line, 3 points are sufficient to define a parabola, 4 points to define a cubic curve and so forth.

Assume that the secret   can be represented as an element   of a finite field   (where   is larger than the number of shares being generated). Randomly choose   elements,  , from   and construct the polynomial  . Compute any   points out on the curve, for instance set   to find points  . Every participant is given a point (a non-zero input to the polynomial, and the corresponding output).[2] Given any subset of   of these pairs,   can be obtained using interpolation, with one possible formula for doing so being  , where the list of points on the polynomial is given as k pairs of the form  . Note that   is equal to the first coefficient of polynomial  .



The following example illustrates the basic idea. Note, however, that calculations in the example are done using integer arithmetic rather than using finite field arithmetic to make the idea easier to understand. Therefore the example below does not provide perfect secrecy and is not a proper example of Shamir's scheme. The next example will explain the problem.


Suppose that the secret to be shared is 1234  .

In this example, the secret will be split into 6 shares  , where any subset of 3 shares   is sufficient to reconstruct the secret.   numbers are taken at random. Let them be 166 and 94.

This yields coefficients   where   is the secret

The polynomial to produce secret shares (points) is therefore:


Six points   from the polynomial are constructed as:


Each participant in the scheme receives a different point (a pair of   and  ). Because   is used instead of   the points start from   and not  . This is necessary because   is the secret.


In order to reconstruct the secret, any 3 points are sufficient

Consider using the 3 points .

Computing the: Lagrange basis polynomials:


Using the formula for polynomial interpolation,   is:


Recalling that the secret is the free coefficient, which means that  , and we are done.

Computationally efficient approachEdit

Using polynomial interpolation to find a coefficient in a source polynomial   using Lagrange polynomials is not efficient, since unused constants are calculated.

Considering this, an optimized formula to use Lagrange polynomials to find   is defined as follows:



Although the simplified version of the method demonstrated above, which uses integer arithmetic rather than finite field arithmetic, works, there is a security problem: Eve gains information about   with every   that she finds.

Suppose that she finds the 2 points   and  . She still does not have   points, so in theory she should not have gained any more information about  . But she could combine the information from the 2 points with the public information:  . Doing so, Eve could perform the following algebra:

  1. Fill the formula for   with   and the value of  
  2. Fill (1) with the values of  's   and  
  3. Fill (1) with the values of  's   and  
  4. Subtract (3)-(2):   and rewrite this as  . Eve knows that   so she starts replacing   in (4) with 0, 1, 2, 3, ... to find all possible values for  :
  5. After checking  , she stops because would get negative values for   with larger values of   (which is impossible because  ). Eve can now conclude  
  6. Now, Eve can replace   by (4) in (2):  . Now, replacing   in (6) by the values found in (5), she gets   which leads her to the information:  

Eve now only has 150 numbers to guess from instead of an infinite quantity of natural numbers.


This is a polynomial curve over a finite field—now the order of the polynomial has seemingly little to do with the shape of the graph.

Geometrically this attack exploits the fact that the order of the polynomial is known and thus gives information into the paths the polynomial take between known points. This reduces possible values of unknown points since the points must lie on a smooth curve, and the polynomial must have coefficients that are natural numbers.

This problem can be fixed by using finite field arithmetic. A field of size   is used. The figure shows a polynomial curve over a finite field. In contrast to a smooth curve it appears disorganised and disjointed.

In practice this is only a small change. A prime   must be chosen that is bigger than the number of participants and every   (including  ). The points on the polynomial must also be calculated as   instead of  .

Everybody who receives a point must also know the value of  , so it is considered to be publicly known. Therefore, one should select a value for   that is not too low to prevent attacks where somebody guesses every possible value for  .

For this example, choose  , so the polynomial becomes   which gives the points:  

This time Eve doesn't gain any information when she finds a   (until she has   points).

Suppose again that Eve finds   and  , and the public information is:  . Attempting the previous attack, Eve can:

  1. Fill the  -formula with   and the value of   and  :  
  2. Fill (1) with the values of  's   and  
  3. Fill (1) with the values of  's   and  
  4. Subtracts (3)-(2):   and rewrites this as  
  5. Using   so she starts replacing   in (4) with 0, 1, 2, 3, ... to find all possible values for  :

This time she is not able to stop because   could be any integer modulo   (even negative if  ) so there are   possible values for  . She knows that   always decreases by 3, so if   was divisible by   she could conclude  . However,   is prime she can not conclude this. Thus, using a finite field avoids this possible attack.

Python exampleEdit

The following Python implementation of Shamir's Secret Sharing is
released into the Public Domain under the terms of CC0 and OWFa:

See the bottom few lines for usage. Tested on Python 2 and 3.

from __future__ import division
from __future__ import print_function

import random
import functools

# 12th Mersenne Prime
# (for this application we want a known prime number as close as
# possible to our security level; e.g.  desired security level of 128
# bits -- too large and all the ciphertext is large; too small and
# security is compromised)
_PRIME = 2 ** 127 - 1
# The 13th Mersenne Prime is 2**521 - 1

_RINT = functools.partial(random.SystemRandom().randint, 0)

def _eval_at(poly, x, prime):
    """Evaluates polynomial (coefficient tuple) at x, used to generate a
    shamir pool in make_random_shares below.
    accum = 0
    for coeff in reversed(poly):
        accum *= x
        accum += coeff
        accum %= prime
    return accum

def make_random_shares(secret, minimum, shares, prime=_PRIME):
    Generates a random shamir pool for a given secret, returns share points.
    if minimum > shares:
        raise ValueError("Pool secret would be irrecoverable.")
    poly = [secret] + [_RINT(prime - 1) for i in range(minimum - 1)]
    points = [(i, _eval_at(poly, i, prime))
              for i in range(1, shares + 1)]
    return points

def _extended_gcd(a, b):
    Division in integers modulus p means finding the inverse of the
    denominator modulo p and then multiplying the numerator by this
    inverse (Note: inverse of A is B such that A*B % p == 1). This can
    be computed via the extended Euclidean algorithm
    x = 0
    last_x = 1
    y = 1
    last_y = 0
    while b != 0:
        quot = a // b
        a, b = b, a % b
        x, last_x = last_x - quot * x, x
        y, last_y = last_y - quot * y, y
    return last_x, last_y

def _divmod(num, den, p):
    """Compute num / den modulo prime p

    To explain this, the result will be such that: 
    den * _divmod(num, den, p) % p == num
    inv, _ = _extended_gcd(den, p)
    return num * inv

def _lagrange_interpolate(x, x_s, y_s, p):
    Find the y-value for the given x, given n (x, y) points;
    k points will define a polynomial of up to kth order.
    k = len(x_s)
    assert k == len(set(x_s)), "points must be distinct"
    def PI(vals):  # upper-case PI -- product of inputs
        accum = 1
        for v in vals:
            accum *= v
        return accum
    nums = []  # avoid inexact division
    dens = []
    for i in range(k):
        others = list(x_s)
        cur = others.pop(i)
        nums.append(PI(x - o for o in others))
        dens.append(PI(cur - o for o in others))
    den = PI(dens)
    num = sum([_divmod(nums[i] * den * y_s[i] % p, dens[i], p)
               for i in range(k)])
    return (_divmod(num, den, p) + p) % p

def recover_secret(shares, prime=_PRIME):
    Recover the secret from share points
    (points (x,y) on the polynomial).
    if len(shares) < 3:
        raise ValueError("need at least three shares")
    x_s, y_s = zip(*shares)
    return _lagrange_interpolate(0, x_s, y_s, prime)

def main():
    """Main function"""
    secret = 1234
    shares = make_random_shares(secret, minimum=3, shares=6)

    print('Secret:                                                     ',
    if shares:
        for share in shares:
            print('  ', share)

    print('Secret recovered from minimum subset of shares:             ',
    print('Secret recovered from a different minimum subset of shares: ',

if __name__ == '__main__':


Some of the useful properties of Shamir's   threshold scheme are:

  1. Secure: The scheme has Information theoretic security.
  2. Minimal: The size of each piece does not exceed the size of the original data.
  3. Extensible: When   is kept fixed, shares   can be dynamically added or deleted without affecting the other pieces, because computing new points on the polynomial does not affect the currently computed points.
  4. Dynamic: Security can be easily enhanced without changing the secret, but by changing the polynomial occasionally (keeping the same free term) and constructing new shares for the participants.
  5. Flexible: In organizations where hierarchy is important, each participant can be assigned different numbers of shares according to their importance inside the organization. For instance, the president could unlock the safe alone, whereas 3 secretaries would be required to combine their shares to unlock the safe.

A known issue in Shamir's Secret Sharing scheme is the verification of correctness of the retrieved shares during the reconstruction process, which is known as verifiable secret sharing. Verifiable secret sharing aims to verify that shareholders are honest and not submitting fake shares.

See alsoEdit


  1. ^ Shamir, Adi (1979), "How to share a secret", Communications of the ACM, 22 (11): 612–613, doi:10.1145/359168.359176, S2CID 16321225
  2. ^ Knuth, D. E. (1997), The Art of Computer Programming, vol. II: Seminumerical Algorithms (3rd ed.), Addison-Wesley, p. 505.

External linksEdit