Security and safety for shared AI by Deep Model Design

The theory part of S3AI comprises mathematical aspects of transfer learning and a novel computational geometric approach for deep model analysis:

Tessellation: study the interdependence between a deep model represented as neural network, its induced (tessellation) geometry in the input space and its separability properties;

Deep Transfer Learning: provide quantitative bounds on the misclassification and dertermine convergence rates in transfer learning settings;

A software platform for devloping and testing shared AI is envisoned that goes beyond standard federated learning by taking transfer learning into account:

Development Platform: to support the configuration, instantiation and orchestration of pipelines for training transfer learning and confidentiality preservation algorithms;

Test Environment: for modeling threat and attack scenarios to evaluate privacy and confidentiality protection and identify vulnerabilities;

S3AI takes up and bundles requirements from the companies, reflects them from the point of view of integrity and privacy threat scenarios, comprising:

Specification of requirements from the company partners’ application scenarios in terms of safety, information security and trust and elaboration of test scenarios for benchmark analysis;

Benchmark analysis regarding privacy and robustness against various threat scenarios and against standard deep learning models;

Guidelines for industrial partners as basis for R&D strategy development regarding the S3AI objectives;