Accenture Global Solutions Limited

Aolin Ding

Amin Hassanzadeh

Malek Ben Salem

Neil Hayden Liberman

Benjamin Glen McCarty

Gal Engelberg

Dan Klein

Hodaya Binyamini

Louis William Divalentin

Amin Hass

Implementations include a computer-implemented method comprising: obtaining data representing observed conditions in an enterprise network, each observed condition being associated with at least one cybersecurity issue, a cybersecurity issue comprising one of (i) a vulnerability comprising an instance of a vulnerable condition or (ii) a weakness that is likely to cause a vulnerability to occur; using a plurality of exploitation prediction models to determine probabilities of exploitation of the cybersecurity issues associated with the observed conditions in the enterprise network, wherein the plurality of exploitation prediction models are trained using a knowledge mesh generated using data from cybersecurity repositories; assigning a priority ranking to each of the observed conditions in the enterprise network based on the respective probabilities of exploitation for the cybersecurity issues associated with the observed conditions; and performing one or more actions to mitigate the observed conditions in the enterprise network based on the priority rankings.

Automated cybersecurity vulnerability prioritization

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.

Privacy-preserving collaborative machine learning training using distributed executable file packages in an untrusted environment

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.

Privacy-preserving machine learning training based on homomorphic encryption using executable file packages in an untrusted environment

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. For example, a server (or cloud-based computing device(s)) may be configured to 'split' an initial ML model into various partial ML models, some of which are provided to client devices for training based on client-specific data. Output data generated during the training at the client devices may be provided to the server for use in training corresponding server-side partial ML models. After training of the partial ML models is complete, the server may aggregate the trained partial ML models to construct an aggregate ML model for deployment to the client devices. Because the client data is not shared with other entities, privacy is maintained, and the splitting of the ML models enables offloading of computing resource-intensive training from client devices to the server.

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Piscataway, NJ, United States of America

Aolin Ding