tree 987573ca169a639c95826ca77fccb9750fb67f5f
parent fca35013e10cb81db65d13d3328b1eca0de5760b
author sebwrede <swrede@know-center.at> 1600270560 +0200
committer baunsgaard <baunsgaard@tugraz.at> 1601382337 +0200

[SYSTEMDS-2668] Fine-Grained Priv Constraint Prop

This PR improves propagation of fine-grained privacy constraints for
matrix multiplications. The PR also provides a new structure for
fine-grained privacy propagations by introducing a "Propagator"
interface which are implemented by different propagator classes. This
interface will be used in the following implementations of privacy
propagation for other operators.

The new matrix multiplication propagation is more efficient than the
previous implementation since it makes an array with the summarized
privacy level of the rows of the first matrix and the columns of the
second matrix.
Furthermore, it takes the operator type into account. This means that
if a row or column contains only a single non-zero value, it cannot be
considered an aggregation, hence the output in case of the
PrivateAggregation privacy level in the input should still be
PrivateAggregation. The rules of propagation is implemented in the
method "PrivacyPropagator.corePropagation", where the comment also
details the privacy "truth table".

- Edit Fine-Grained Constraint Propagation in Matrix Multiplications
- The new version will take operator type into account when propagating
  and will summarize the privacy level of rows and columns of the input
  matrices to make a faster propagation. The new implementation needs
  further test cases, which will be added in future commits.
- Add Tests of Matrix Multiplication Privacy Propagation
- Refactor Matrix Multiplication Propagation By Introducing
  the Propagator Interface
- Add Optimized PrivateFirst Propagator

Closes #1060