Programmatic interface to SHEEP
Project description
========
matSHEEP
========
This library is a programmatic interface in python to generate a circuit for the bigger and more useful SHEEP library.
The library has a few data types :
* variables - A single bit (Could also be used as a normal scalar)
* enc_vec - One dimensional bit vector (Could be used a one dimensional vector of any data type)
* enc_mat - Two dimensional bit matrix (Could be used a one dimensional vector of any data type)
* enc_tensor3 - Three dimensional bit tensor.
To create a circuit, the basic class to inherit is ``mini_mod`` in ``mathsheep.interactions``. To add more components, you can use ``self.add(component)`` inside the ``create`` function as shown below.::
class oneb_adder(mini_mod):
def __init__(self, name, inputs, outputs, nb=None,
randomize_temps=1, carry=True):
mini_mod.__init__(self, name, inputs, outputs)
self.create(...)
def create(self, ...):
self.add(..)
Two types of components can be added.
* Assignments (``from matSHEEP.interactions``):
- mono_assign
+ alias
+ negate
- bi_assign
+ xor
+ and
+ or
+ constand
- tri_assign
+ mux
* Other mini_mods
There are a few predefined mini_mods. They can be found in
* ``matSHEEP.reusable_modules``
- oneb_adder - Add two bits
- nb_adder - Adders x and y with incoming carrt where input is ``[cin x y]``
- nb_adder_xy - Adds x and y with ``input = (x, y)``
- compare_cp - Compares ciphertext with plaintext with ``input = (c,p)``
* ``matSHEEP.functions``
- reduce_add - Counts the number of ones in a bit vector.
* ``matSHEEP.nn_layer``
- sign_fn
- linear_layer_1d - Inner Product of a weight vector with encrypted bit vector followed by a sign function.
- linear_layer - Inner Product of a weight matrix with an encrypted bit vector followed by a sign function.
- conv_layer - A convolution Layer. (Look at examples)
* ``matSHEEP.vector_ops``
- vec_mono_op_cond - Takes a plaintext ``cond`` vector, a plaintext tuple ``ass_types`` containing only ``alias`` and ``negate`` as values and an encrypted bit vector ``input``. It outputs an encrypted bit vector where the ith position has the ``ass_types[cond[idx]]`` operation applied on ``input[idx]``.
- Similar operation for matrix and tensor.
matSHEEP
========
This library is a programmatic interface in python to generate a circuit for the bigger and more useful SHEEP library.
The library has a few data types :
* variables - A single bit (Could also be used as a normal scalar)
* enc_vec - One dimensional bit vector (Could be used a one dimensional vector of any data type)
* enc_mat - Two dimensional bit matrix (Could be used a one dimensional vector of any data type)
* enc_tensor3 - Three dimensional bit tensor.
To create a circuit, the basic class to inherit is ``mini_mod`` in ``mathsheep.interactions``. To add more components, you can use ``self.add(component)`` inside the ``create`` function as shown below.::
class oneb_adder(mini_mod):
def __init__(self, name, inputs, outputs, nb=None,
randomize_temps=1, carry=True):
mini_mod.__init__(self, name, inputs, outputs)
self.create(...)
def create(self, ...):
self.add(..)
Two types of components can be added.
* Assignments (``from matSHEEP.interactions``):
- mono_assign
+ alias
+ negate
- bi_assign
+ xor
+ and
+ or
+ constand
- tri_assign
+ mux
* Other mini_mods
There are a few predefined mini_mods. They can be found in
* ``matSHEEP.reusable_modules``
- oneb_adder - Add two bits
- nb_adder - Adders x and y with incoming carrt where input is ``[cin x y]``
- nb_adder_xy - Adds x and y with ``input = (x, y)``
- compare_cp - Compares ciphertext with plaintext with ``input = (c,p)``
* ``matSHEEP.functions``
- reduce_add - Counts the number of ones in a bit vector.
* ``matSHEEP.nn_layer``
- sign_fn
- linear_layer_1d - Inner Product of a weight vector with encrypted bit vector followed by a sign function.
- linear_layer - Inner Product of a weight matrix with an encrypted bit vector followed by a sign function.
- conv_layer - A convolution Layer. (Look at examples)
* ``matSHEEP.vector_ops``
- vec_mono_op_cond - Takes a plaintext ``cond`` vector, a plaintext tuple ``ass_types`` containing only ``alias`` and ``negate`` as values and an encrypted bit vector ``input``. It outputs an encrypted bit vector where the ith position has the ``ass_types[cond[idx]]`` operation applied on ``input[idx]``.
- Similar operation for matrix and tensor.
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