Programmatic interface to SHEEP
Project description
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.
1. Assignments (`from matSHEEP.interactions`):
* mono_assign
* alias
* negate
* bi_assign
* xor
* and
* or
* constand
* tri_assign
* mux
2. Other mini_mods
There are a few predefined mini_mods. They can be found in
1. `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)`
2. `matSHEEP.functions`
* reduce_add - Counts the number of ones in a bit vector.
3. `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)
4. `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.
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.
1. Assignments (`from matSHEEP.interactions`):
* mono_assign
* alias
* negate
* bi_assign
* xor
* and
* or
* constand
* tri_assign
* mux
2. Other mini_mods
There are a few predefined mini_mods. They can be found in
1. `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)`
2. `matSHEEP.functions`
* reduce_add - Counts the number of ones in a bit vector.
3. `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)
4. `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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