matSHEEP 1.56

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.

• 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.

You can also visualize the circuits you create. test.sheep is a circuit file.:

import sys
import matSHEEP.create_graph as cg
complete_node = cg.get_circuit_graph('./test.sheep')
ng = cg.networkx_graph(complete_node)
ng.draw()

And you can get

gFor more high level operations and results using layers of Neural Networks visit this markdown