| commit | 7c9694a15dd174342a3ef6528cf0f9bda4455e60 | [log] [tgz] |
|---|---|---|
| author | sebwrede <swrede@know-center.at> | Wed Sep 16 17:36:00 2020 +0200 |
| committer | baunsgaard <baunsgaard@tugraz.at> | Tue Sep 29 14:25:37 2020 +0200 |
| tree | 987573ca169a639c95826ca77fccb9750fb67f5f | |
| parent | fca35013e10cb81db65d13d3328b1eca0de5760b [diff] |
[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
Overview: SystemDS is a versatile system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
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Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
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Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution.
