Abstract: Data privacy using federated learning is provided by receiving one or more instance(s) of a master RL agent model and training the instance(s) of the master RL agent model on a corresponding graph, thereby generating corresponding sets of RL model weights. One or more information gains corresponding to one or more software stacks are generated. The information gain(s) and the RL model weight(s) are transmitted to a central server to enable the central server to update the master RL agent model based on the information gain(s) and the RL model weights.

Abstract: Methods, systems and computer program products are provided for integrating risk and threat intelligence from various sources, to provide real-time awareness of potential threats to a computer network, which are now described herein in terms of an example enterprise system.

Abstract: Methods, systems and computer program products are provided for optimizing resources privately. An initial information gain corresponding to an initial client embedding dataset is computed and a machine learning model is trained based on the initial client embedding data set to generate at least one initial attack path in the initial graph data. A second information gain corresponding to a second client embedding data set is computed. A difference between the first information gain and the second information gain is computed. The machine learning model is trained if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data.

Abstract: A fully integrated triboelectric energy harvesting system can comprise a triboelectric generator, an electrical energy harvesting system, and a power amalgamation circuit. The triboelectric generator can comprise triboelectric materials, conductive contacts, and a mechanical motion transformer.