Abstract: A device may receive a machine learning model and training data utilized to train the machine learning model, and may perform a data veracity assessment of the training data to identify and remove poisoned data from the training data. The device may perform an adversarial assessment of the machine learning model to generate adversarial attacks and to provide defensive capabilities for the adversarial attacks, and may perform a membership inference assessment of the machine learning model to generate membership inference attacks and to provide secure training data as a defense for the membership inference attacks. The device may perform a model extraction assessment of the machine learning model to identify model extraction vulnerabilities and to provide a secure application programming interface as a defense to the model extraction vulnerabilities, and may perform actions based on results of one or more of the assessments.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support secure training of machine learning (ML) models that preserves privacy in untrusted environments using distributed executable file packages. The executable file packages may include files, libraries, scripts, and the like that enable a cloud service provider configured to provide ML model training based on non-encrypted data to also support homomorphic encryption of data and ML model training with one or more clients, particularly for a diagnosis prediction model trained using medical data. Because the training is based on encrypted client data, private client data such as patient medical data may be used to train the diagnosis prediction model without exposing the client data to the cloud service provider or others. Using homomorphic encryption enables training of the diagnosis prediction model using encrypted data without requiring decryption prior to training.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cooperative training of machine learning (ML) models that preserves privacy in untrusted environments using distributed executable file packages. The executable file packages may include files, libraries, scripts, and the like that enable a cloud service provider configured to provide server-side ML model training to also support cooperative ML model training with multiple clients, particularly for a fraud prediction model for financial transactions. Because the cooperative training includes the clients training respective ML models and the server aggregating the trained ML models, private client data such as financial transaction data may be used to train the fraud prediction model without exposing the client data to others. Such cooperative ML model training enables offloading of computing resource-intensive training from client devices to the server and may train a more robust fraud detection model.

Abstract: A device may receive training data for training a first machine learning model, a second machine learning model, and a third machine learning model and may train the first machine learning model, the second machine learning model, and the third machine learning model with the training data. The device may receive input content and may process the input content, with the first machine learning model, the second machine learning model, and the third machine learning model, to generate a first model result, a second model result, and a third model result, respectively. The device may process the first model result, the second model result, and the third model result, with an aggregation model, to generate a detection result indicating whether the particular input content is a deepfake or is real and may perform one or more actions based on the detection result.