Private artificial neural networks with trusted execution environments and quadratic homomorphic encryption

A host processing system for remote training of an artificial neural network, the host processing system comprising a trusted execution environment (TEE) and an untrusted processing system (UPS) and being configured to:

• run a trusted process deployed in the TEE that computes a key-pair for a homomorphic encryption scheme and share the public key (PK) of the key-pair with an untrusted process running on the UPS;
• and
• split the training procedure of the ANN between the untrusted process and the trusted process, wherein the untrusted process is configured to compute encrypted inputs to the neurons of the ANN by means of the homomorphic encryption scheme, while the trusted process is configured to compute outputs of the neurons based on the respective encrypted inputs to the neurons as provided by the untrusted process.

A non-transitory computer readable medium for remote training of an artificial neural network with a host processing system comprising a trusted execution environment (TEE) and an untrusted processing system (UPS) the medium comprising program code for configuring the host processing system to:

• run a trusted process deployed in the TEE that computes a key-pair for a homomorphic encryption scheme and share the public key (PK) of the key-pair with an untrusted process running on the UPS;
• and
• split the training procedure of the ANN between the untrusted process and the trusted process, wherein the untrusted process is configured to compute encrypted inputs to neurons of the ANN by means of the homomorphic encryption scheme, while the trusted process is configured to compute outputs of the neurons based on the respective encrypted inputs to the neurons as provided by the untrusted process.