Abstract: Systems and methods for code analysis are provided. A system receives, from a first data owner system, an input indicating one or more privacy constraints, wherein the one or more privacy constraints indicate one or more rules for operating on a sensitive data set in a data store associated with the first data owner system. The system receives data comprising code configured to operate on the sensitive data set. The system apples a first set of one or more machine-learning-trained models to process the first input and the data comprising the code to generate code analysis output data, wherein the code analysis output data comprises an indication of whether the code satisfies the one or more privacy constraints. The system generates and transmits, based on the code analysis output data, an instruction indicating whether to execute the code to operate on the sensitive data.

Abstract: Provided herein are systems and methods for federated machine learning performed by a central system communicatively coupled to plurality of satellite systems that implement privacy-preserving techniques. Synthetic data generated at respective satellite systems based on the actual data of the satellite systems can be utilized to generate data processing rules that can be applied to the actual data and used to develop a central machine learning model. The systems and methods disclosed herein can be used for both horizontal or vertical federated machine learning by implementing an alignment algorithm as necessary. Insights based on synthetic data and/or the alignment algorithm can be used to develop a central machine learning model without accessing any actual data values directly. Local models can be generated by training the central machine learning model at respective satellite sites and then aggregated at the central system, without transmitting the actual data from the respective satellite systems.

Abstract: Provided herein are systems and methods for vertical federated machine learning. Vertical federated machine learning can be performed by a central system communicatively coupled to a plurality of satellite systems. The central system can receive encrypted data from the satellite systems and apply a transformation that transforms the encrypted data into transformed data. The central system can identify matching values in the transformed data and generate a set of location indices that indicate one or more matching values in the transformed data. The central system can transmit instructions to the satellite systems to access data stored at locations indicated by the location indices and to train a machine learning model using data associated with said locations.

Abstract: Systems and methods for federated machine learning are provided. A central system receives satellite analytics artifacts from a plurality of satellite site systems and generates a central machine learning model based on the satellite analytics artifacts. A plurality of federated machine learning epochs are executed. At each epoch, the central system transmitting the central machine learning model to the plurality of satellite site systems, and then receives in return, from each satellite site system, a respective set of satellite values for a set of weights of the model, wherein the satellite values are generated by the respective satellite site system based on a respective local dataset of the satellite site system. At each epoch, the central system then generates an updated version of the central machine learning model based on the satellite values received from the satellite site systems.