Machine Learning Quickstart

This document is a short introduction to running privacy-preserving logistic regression in MP-SPDZ. It assumes that you have the framework already installed as explained in the installation instructions. For more information on how to run machine learning algorithms in MP-SPDZ, see the full machine learning section.

The easiest way to use is to put Python code in an .mpc in Programs/Source, for example Programs/Source/foo.mpc. Put the following code there to use the breast cancer dataset:

X = sfix.input_tensor_via(0, [[1, 2, 3], # 2 samples
                              [11, 12, 13]])
y = sint.input_tensor_via(0, [0, 1]) # 2 labels

from Compiler import ml
log = ml.SGDLogistic(100)
log.fit(X, y)

print_ln('%s', log.predict(X).reveal())

The first two lines make the data available to the secure computation. The next lines create a logistic regression instance and train it (for one hundred epochs). Finally, the last line uses the instances for predictions and outputs the results.

After adding all the above code to Programs/Source/foo.mpc, you can run it either insecurely:

Scripts/compile-emulate.py foo

or securely with three parties on the same machine:

Scripts/compile-run.py -E ring foo

The first call should give the following output:

$ Scripts/compile-emulate.py foo
Default bit length: 63
Default security parameter: 40
Compiling file Programs/Source/foo.mpc
Writing binary data to Player-Data/Input-Binary-P0-0
Setting learning rate to 0.01
Using SGD
Initializing dense weights in [-1.224745,1.224745]
Writing to Programs/Bytecode/foo-multithread-1.bc
2 runs per epoch
Writing to Programs/Bytecode/foo-multithread-3.bc
Writing to Programs/Bytecode/foo-multithread-4.bc
Writing to Programs/Bytecode/foo-multithread-5.bc
Initializing dense weights in [-1.224745,1.224745]
Writing to Programs/Bytecode/foo-multithread-7.bc
Writing to Programs/Bytecode/foo-multithread-8.bc
Writing to Programs/Bytecode/foo-multithread-9.bc
Writing to Programs/Schedules/foo.sch
Writing to Programs/Bytecode/foo-0.bc
Hash: 33f8d22d99960897f41fb2da31e7f5a0501d2e1071789e52d73b4043e5343831
Program requires at most:
           8 integer inputs from player 0
       61054 integer bits
      190109 integer triples
         200 matrix multiplications (1x3 * 3x1)
         200 matrix multiplications (3x1 * 1x1)
           1 matrix multiplications (2x3 * 3x1)
       28406 virtual machine rounds
Using security parameter 40
Trying to run 64-bit computation
Using SGD
done with epoch 99
[0, 1]
The following benchmarks are including preprocessing (offline phase).
Time = 0.0250086 seconds

See the documentation for further options such as different protocols or running remotely and the machine learning section for other machine learning methods.