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threshold-crypto

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A python library for ElGamal-based threshold decryption.

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# Threshold cryptography library

A stateless library which offers functionality for ElGamal-based threshold decryption with centralized or distributed key generation.

Threshold decryption means a message can be encrypted using a simple public key, but for decryption at least t out of n
share owners must collaborate to decrypt the message.

A hybrid approach (using [pynacl](https://pynacl.readthedocs.io) for symmetric encryption and 
[PyCryptodome](https://pycryptodome.readthedocs.io) for ECC operations) is used for message encryption and decryption.
Therefore there are no limitations regarding message lengths or format. Additionally the integrity of a message is
secured by using the AE-scheme, meaning changes to some parts of the ciphertext, to partial decryptions or even
dishonest share owners can be detected.

**Warning**: This library has never been (independently) audited and must not be used for productive applications.

## Usage

Import the library:

>>> import threshold_crypto as tc

### Parameter Generation

Generate required parameters:

>>> curve_params = tc.CurveParameters()
    >>> thresh_params = tc.ThresholdParameters(t=3, n=5)

The `CurveParameters` describe the elliptic curve the operations are performed on. 
The `ThresholdParameters` determine the number of created shares `n` and the number of required participants for the decryption operation `t`.

### Centralized Key Generation

The public key and shares of the private key can be computed in a centralized manner by a trusted third party:

>>> pub_key, key_shares = tc.create_public_key_and_shares_centralized(curve_params, thresh_params)

### Distributed Key Generation

But they can also be computed via a distributed key generation (DKG) protocol following "A threshold cryptosystem without a trusted party" by Pedersen (1991).
This involves multiple steps performed by all participants in collaboration.
The following example code uses lists to illustrate this, but has to be distributed over the different participant applications and machines in practice.

The first step is the participant initialization:

>>> participant_ids = list(range(1, thresh_params.n + 1))
    >>> participants = [tc.Participant(id, participant_ids, curve_params, thresh_params) for id in participant_ids]

Next each participant broadcasts a closed commitment to a share of the later public key to the other participants:

>>> for pi in participants:
    ...     for pj in participants:
    ...         if pj != pi:
    ...             closed_commitment = pj.closed_commmitment()
    ...             pi.receive_closed_commitment(closed_commitment)

After each participant has received all closed commitments they broadcast their open commitments:

>>> for pi in participants:
    ...     for pj in participants:
    ...         if pj != pi:
    ...             open_commitment = pj.open_commitment()
    ...             pi.receive_open_commitment(open_commitment)

Afterwards each participant should be able to compute the same public key:

>>> public_key = participants[0].compute_public_key()
    >>> for pk in [p.compute_public_key() for p in participants[1:]]:
    ...     assert public_key == pk

Now each participant broadcasts his F_ij (following the notation of Pedersen) values to all other participants.
These values are used to commit to the secret s_ij values send and received in the next step.

>>> for pi in participants:
    ...     for pj in participants:
    ...         if pj != pi:
    ...             F_ij = pj.F_ij_value()
    ...             pi.receive_F_ij_value(F_ij)

Ongoing each participant sends a share of his private secret value to every other participant SECRETLY.
**Attention**: The library currently does NOT enforce this secrecy. Clients have to provide this functionality themselves.
This is heavily important and the protocol does not fulfill its security guarantees otherwise (meaning it is completely broken).

>>> for pi in participants:
    ...     for pj in participants:
    ...         if pj != pi:
    ...             s_ij = pj.s_ij_value_for_participant(pi.id)
    ...             pi.receive_sij(s_ij)

Finally each participant can compute his `KeyShare`, which can be used for computing `PartialDecryption` or `PartialReEncryptionKey` objects.

>>> shares = [p.compute_share() for p in participants]

### Encryption

A message is encrypted using the public key:

>>> message = 'Some secret message to be encrypted!'
    >>> encrypted_message = tc.encrypt_message(message, pub_key)

### Computing Partial Decryptions

`t` share owners compute partial decryptions of a ciphertext using their shares:

>>> partial_decryptions = []
    >>> for participant in [0, 2, 4]:
    ...     participant_share = key_shares[participant]
    ...     partial_decryption = tc.compute_partial_decryption(encrypted_message, participant_share)
    ...     partial_decryptions.append(partial_decryption)

### Combining Partial Decryptions

Combine these partial decryptions to recover the message:

>>> decrypted_message = tc.decrypt_message(partial_decryptions, encrypted_message, thresh_params)
    >>> print(decrypted_message)
    Some secret message to be encrypted!

### Updating Ciphertexts

When the participants of the scheme change (adding participants, removing participants, ...) existing ciphertexts can be re-encrypted to be decryptable with the new shares.

First, create the new shares (for simplicity the centralized approach is shown here, in practice you want to use distributed key generation):

>>> new_pub_key, new_key_shares = tc.create_public_key_and_shares_centralized(curve_params, thresh_params)

A third party computes non-secret values required for the generation of the re-encryption key for `max(t_old, t_new)` participants involved in the re-encryption key generation:

>>> t_max = thresh_params.t
    >>> old_indices = [key_share.x for key_share in key_shares][:t_max]
	>>> new_indices = [key_share.x for key_share in new_key_shares][:t_max]

>>> coefficients = []
	>>> for p in range(1, t_max + 1):
	...     old_lc = tc.lagrange_coefficient_for_key_share_indices(old_indices, p, curve_params)
	...     new_lc = tc.lagrange_coefficient_for_key_share_indices(new_indices, p, curve_params)
	...     coefficients.append((old_lc, new_lc))
	
 A number of `max(t_old, t_new)` participants now compute their partial re-encryption keys using these non-secret values and his shares:

>>> partial_re_enc_keys = []
    >>> for p in range(t_max):
    ...     old_share = key_shares[p]
    ...     new_share = new_key_shares[p]
    ...     old_lc, new_lc = coefficients[p]
    ...     partial_re_enc_key = tc.compute_partial_re_encryption_key(old_share, old_lc, new_share, new_lc)
    ...     partial_re_enc_keys.append(partial_re_enc_key)
 
The third party computes the re-encryption key by combining the partial re-encryption keys:
 
	>>> re_enc_key = tc.combine_partial_re_encryption_keys(partial_re_enc_keys, pub_key, new_pub_key, thresh_params, thresh_params)

The encrypted message is re-encrypted to be decryptable by the new shares:

>>> new_encrypted_message = tc.re_encrypt_message(encrypted_message, re_enc_key)

Decryption can now be performed using the new shares:

>>> reconstruct_shares = [new_key_shares[i] for i in [0, 2, 4]]
    >>> partial_decryptions = [tc.compute_partial_decryption(new_encrypted_message, share) for share in reconstruct_shares]
    >>> decrypted_message = tc.decrypt_message(partial_decryptions, new_encrypted_message, thresh_params)
    >>> print(decrypted_message)
    Some secret message to be encrypted!

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