Paddle FL MPC (PFM) is a framework for privacy-preserving deep learning based on PaddlePaddle. It follows the same running mechanism and programming paradigm with PaddlePaddle, while using secure multi-party computation (MPC) to enable secure training and prediction.
With PFM, it is easy to train models or conduct prediction as on PaddlePaddle over encrypted data, without the need for cryptography expertise. Furthermore, the rich industry-oriented models and algorithms built on PaddlePaddle can be smoothly migrated to secure versions on PFM with little effort.
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@@ -148,12 +150,13 @@ In PFM, data and models from IPs will be encrypted using Secret-Sharing[10], and
As in PaddlePaddle, a training or inference job can be separated into the compile-time phase and the run-time phase:
##### 1. Compile time
***MPC environment specification**: a user needs to choose a MPC protocol, and configure the network settings. In current version, PFE provides only the "ABY3" protocol. More protocol implementation will be provided in future.
***MPC environment specification**: a user needs to choose a MPC protocol, and configure the network settings. In current version, PFM provides only the "ABY3" protocol. More protocol implementation will be provided in future.
***User-defined job program**: a user can define the machine learning model structure and the training strategies (or inference task) in a PFM program, using the secure operators.
##### 2. Run time
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A PFM program is exactly a PaddlePaddle program, and will be executed as normal PaddlePaddle programs. For example, in run-time a PFM program will be transpiled into ProgramDesc, and then be passed to and run by the Executor. The main concepts in the run-time phase are as follows:
***Computing nodes**: a computing node is an entity corresponding to a Computing Party. In real deployment, it can be a bare-metal machine, a cloud VM, a docker or even a process. PFM requires exactly three computing nodes in each run, which is determined by the underlying ABY3 protocol. A PFM program will be deployed and run in parallel on all three computing nodes.
***Operators using MPC**: PFM provides typical machine learning operators in `paddle_fl.mpc` over encrypted data. Such operators are implemented upon PaddlePaddle framework, based on MPC protocols like ABY3. Like other PaddlePaddle operators, in run time, instances of PFE operators are created and run in order by Executor.
***Operators using MPC**: PFM provides typical machine learning operators in `paddle_fl.mpc` over encrypted data. Such operators are implemented upon PaddlePaddle framework, based on MPC protocols like ABY3. Like other PaddlePaddle operators, in run time, instances of PFM operators are created and run in order by Executor.
