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.

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

https://raw.githubusercontent.com/amartya18x/matSHEEP/master/images/LL4.png

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

Metadata

Release files for matSHEEP 1.56

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for matSHEEP 1.56
File	Size	Uploaded
matSHEEP-1.56.tar.gz	164.3 kB	Jun 10, 2018	Details

Release files / matSHEEP-1.56.tar.gz

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