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MechFroG88 / PyMIFE

Functional encryption library for python
MIT License
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cryptography functional-encryption python

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PyMIFE

Multi input functional encryption library for python

Installation

pip install pymife

Schemes

Single input inner product

  1. (Selective Secure) DDH based scheme from https://eprint.iacr.org/2015/017.pdf
  2. (Selective Secure) LWE based scheme from https://eprint.iacr.org/2015/017.pdf
  3. (Adaptive Secure) Damgard based scheme from https://eprint.iacr.org/2015/608.pdf
  4. (Adaptive Secure) LWE based scheme from https://eprint.iacr.org/2015/608.pdf

Single input inner product (Function Hiding)

  1. (Adaptive Secure) DDH based scheme from https://eprint.iacr.org/2016/440.pdf

Single input inner product (Quadratic)

  1. (Adaptive Secure) DDH based scheme from https://eprint.iacr.org/2018/206.pdf

Multi input inner product

  1. (Adaptive Secure) Damgard based scheme from https://eprint.iacr.org/2017/972.pdf

Multi client inner product

  1. (Adaptive Secure with Random Oracle) DDH based scheme from https://eprint.iacr.org/2017/989.pdf
  2. (Adaptive Secure) Damgard based scheme from https://eprint.iacr.org/2019/487.pdf, using Damgard single input

Note

Usage

Single input inner product

DDH based scheme

from mife.single.selective.ddh import FeDDH

n = 10
x = [i for i in range(n)]
y = [i + 10 for i in range(n)]
key = FeDDH.generate(n)
c = FeDDH.encrypt(x, key)
sk = FeDDH.keygen(y, key)
m = FeDDH.decrypt(c, key.get_public_key(), sk, (0, 1000))

LWE based scheme

from mife.single.selective.lwe import FeLWE

n = 10
x = [i - 10 for i in range(n)]
y = [i for i in range(n)]
key = FeLWE.generate(n, 4, 4)
c = FeLWE.encrypt(x, key)
sk = FeLWE.keygen(y, key)
m = FeLWE.decrypt(c, key.get_public_key(), sk) % key.p

(Adaptive Secure) Damgard based scheme

from mife.single.damgard import FeDamgard

n = 10
x = [i for i in range(n)]
y = [i + 10 for i in range(n)]
key = FeDamgard.generate(n)
c = FeDamgard.encrypt(x, key)
sk = FeDamgard.keygen(y, key)
m = FeDamgard.decrypt(c, key.get_public_key(), sk, (0, 1000))

(Adaptive Secure) LWE based scheme

from mife.single.lwe import FeLWE

n = 10
x = [i - 10 for i in range(n)]
y = [i for i in range(n)]
key = FeLWE.generate(n, 4, 4)
c = FeLWE.encrypt(x, key)
sk = FeLWE.keygen(y, key)
m = FeLWE.decrypt(c, key.get_public_key(), sk)

Single input inner product (Function Hiding)

DDH based scheme

from mife.single.fhiding.ddh import FeDDH

n = 4
x = [i for i in range(n)]
y = [i + 10 for i in range(n)]
key = FeDDH.generate(n)
c = FeDDH.encrypt(x, key)
sk = FeDDH.keygen(y, key)
m = FeDDH.decrypt(c, key.get_public_key(), sk, (0, 1000))

Single input inner product (Quadratic)

DDH based scheme

from mife.single.quadratic.ddh import FeDDH

n = 2
x = [i + 2 for i in range(n)]
y = [i + 3 for i in range(n)]
f = [[i + j + 1 for j in range(n)] for i in range(n)]
key = FeDDH.generate(n)
c = FeDDH.encrypt(x, y, key)
sk = FeDDH.keygen(f, key)
m = FeDDH.decrypt(c, key.get_public_key(), sk, (0, 1000))

Multi input inner product

Damgard based scheme

from mife.multi.damgard import FeDamgardMulti

n = 3
m = 5
x = [[i + j for j in range(m)] for i in range(n)]
y = [[i - j + 10 for j in range(m)] for i in range(n)]
key = FeDamgardMulti.generate(n, m)
cs = [FeDamgardMulti.encrypt(x[i], key.get_enc_key(i)) for i in range(n)]
sk = FeDamgardMulti.keygen(y, key)
m = FeDamgardMulti.decrypt(cs, key.get_public_key(), sk, (0, 2000))

Using Curve25519

from mife.multi.damgard import FeDamgardMulti
from mife.data.curve25519 import Curve25519

n = 25
m = 25
x = [[i * 10 + j for j in range(m)] for i in range(n)]
y = [[i - j - 5 for j in range(m)] for i in range(n)]
key = FeDamgardMulti.generate(n, m, Curve25519)
cs = [FeDamgardMulti.encrypt(x[i], key.get_enc_key(i)) for i in range(n)]
sk = FeDamgardMulti.keygen(y, key)
res = FeDamgardMulti.decrypt(cs, key.get_public_key(), sk, (-10000000, 10000000))

Using P256 from fastecdsa

from mife.multi.damgard import FeDamgardMulti
from mife.data.fastecdsa_wrapper import WrapCurve
from fastecdsa.curve import P256

n = 25
m = 25
x = [[i * 10 + j for j in range(m)] for i in range(n)]
y = [[i - j - 5 for j in range(m)] for i in range(n)]
key = FeDamgardMulti.generate(n, m, WrapCurve(P256))
cs = [FeDamgardMulti.encrypt(x[i], key.get_enc_key(i)) for i in range(n)]
sk = FeDamgardMulti.keygen(y, key)
res = FeDamgardMulti.decrypt(cs, key.get_public_key(), sk, (-10000000, 10000000))

Multi client inner product

(Random Oracle) DDH based scheme

from mife.multiclient.rom.ddh import FeDDHMultiClient

n = 3
m = 5
x = [[i + j for j in range(m)] for i in range(n)]
y = [[i - j + 10 for j in range(m)] for i in range(n)]
tag = b"testingtag123"
key = FeDDHMultiClient.generate(n, m)
cs = [FeDDHMultiClient.encrypt(x[i], tag, key.get_enc_key(i)) for i in range(n)]
sk = FeDDHMultiClient.keygen(y, key)
m = FeDDHMultiClient.decrypt(cs, tag, key.get_public_key(), sk, (0, 2000))

Damgard based scheme

from mife.multiclient.damgard import FeDamgardMultiClient

n = 3
m = 5
x = [[i + j for j in range(m)] for i in range(n)]
y = [[i - j + 10 for j in range(m)] for i in range(n)]
tag = b"testingtag123"
key = FeDamgardMultiClient.generate(n, m)
cs = [FeDamgardMultiClient.encrypt(x[i], tag, key.get_enc_key(i), key.get_public_key()) for i in range(n)]
sk = FeDamgardMultiClient.keygen(y, key)
m = FeDamgardMultiClient.decrypt(cs, key.get_public_key(), sk, (0, 2000))
Export Keys
from mife.multiclient.rom.ddh import FeDDHMultiClient
import json

n = 3
m = 5
x = [[i + j for j in range(m)] for i in range(n)]
y = [[i - j + 10 for j in range(m)] for i in range(n)]
tag = b"testingtag123"
key = FeDDHMultiClient.generate(n, m)
cs = [FeDDHMultiClient.encrypt(x[i], tag, key.get_enc_key(i)) for i in range(n)]
sk = FeDDHMultiClient.keygen(y, key)
print(f"enc_key = {json.dumps([key.get_enc_key(i).export() for i in range(n)])}")
print(f"msk = {json.dumps(key.export())}")
print(f"ct = {[json.dumps(cs[i].export()) for i in range(n)]}")
print(f"secret_key = {json.dumps(sk.export())}")
print(f"pub_key = {json.dumps(key.get_public_key().export())}")

Customize

All of the DDH and Damgard schemes support custom group. You can implement your own group class by extending /src/mife/data/group.py as base class.

To use custom group, simply pass the group class to the generate function.

This library has implemented prime order group and curve25519 group.

For Random Oracle Model MCFE-DDH scheme, you can also supply your own hash function by using the same signature as the default hash function found in /src/mife/multiclient/ddh.py.

For Function Hiding and Quadratic scheme, you can supply your own pairing group better efficiency.

References