pymife 0.0.14

Python Functional Encryption Library

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
3. (Adaptive Secure with Random Oracle) Decentralized DDH based scheme from https://eprint.iacr.org/2019/020.pdf

Private Non-interactive Aggregation (PALIA)

1. (Adaptive Secure with Random Oracle) Damgard based scheme

Note

• The implementation of these schemes are not fully optimized and not peer-reviewed, recommended to only use for research / testing purpose.
• More schemes will be added in the future

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))

(Random Oracle) Decentralized DDH based scheme

from mife.multiclient.decentralized.ddh import FeDDHMultiClientDec

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"
pub = FeDDHMultiClientDec.generate(n, m)
keys = [pub.generate_party(i) for i in range(n)]

for i in range(n):
    for j in range(n):
        if i == j: continue
        keys[i].exchange(j, keys[j].get_exc_public_key())

for i in range(n):
    keys[i].generate_share()

cs = [FeDDHMultiClientDec.encrypt(x[i], tag, keys[i]) for i in range(n)]
sk = [FeDDHMultiClientDec.keygen(y, keys[i]) for i in range(n)]
m = FeDDHMultiClientDec.decrypt(cs, tag, pub, sk, (0, 2000))

Private Non-interactive Aggregation (PALIA)

from mife.multiclient.decentralized.palia import Palia

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"
pub = Palia.generate(n, m)
keys = [pub.generate_party(i) for i in range(n)]
for i in range(n):
    for j in range(n):
        if i == j: continue
        keys[i].exchange(j, keys[j].get_exc_public_key())
        
for i in range(n):
    keys[i].generate_share()
    
mk = Palia.generate_query_key(pub)
pk = mk.getPublicKey()
enc_y = Palia.encrypt_query(y, pk, pub)

cs = [Palia.encrypt(x[i], tag, keys[i]) for i in range(n)]
sk = [Palia.keygen(enc_y, keys[i]) for i in range(n)]
m = Palia.decrypt(cs, tag, pub, sk, y, mk, (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

• https://eprint.iacr.org/2015/017.pdf
• https://eprint.iacr.org/2015/608.pdf
• https://eprint.iacr.org/2016/440.pdf
• https://eprint.iacr.org/2017/972.pdf
• https://eprint.iacr.org/2017/989.pdf
• https://eprint.iacr.org/2018/206.pdf
• https://eprint.iacr.org/2019/020.pdf
• https://eprint.iacr.org/2019/487.pdf
• https://github.com/fentec-project/CiFEr/blob/master/src/innerprod/simple/lwe.cr2html