matSHEEP 1.1.0

Programmatic interface to SHEEP

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.