Nec Corporation

Claudio Soriente

Ghassan Karame

Giorgia Azzurra Marson

Samira Briongos

Dario Fiore

Elina Van Kempen

A method training an artificial neural network (ANN) on a remote host computes, using a trusted process deployed in a trusted execution environment (TEE) on the remote host, a key-pair for a homomorphic encryption scheme and shares, by the trusted process, the public key (PK) of the key-pair with an untrusted process deployed on the remote host. The method splits the training procedure of the ANN between the untrusted process and the trusted process, wherein the untrusted process computes encrypted inputs to neurons of the ANN by means of the homomorphic encryption scheme, while the trusted process computes outputs of the neurons based on the respective encrypted inputs to the neurons as provided by the untrusted process.

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

A method for performing computation of a function over encrypted data by a computation entity is provided. According to the method, the computation of a quadratic function is performed by the computation entity, the encrypted data are encrypted by a linearly homomorphic encryption scheme, and a homomorphic message authentication code is used for providing verifiable computation on encrypted data.

Method and computation entity for performing computation of a function over encrypted data

A method for secure aggregation using public randomness is provided. The method includes determining, based on the public randomness from a random service beacon, a set of super-clients from a plurality of clients. The method further includes obtaining masked inputs from the clients and aggregated random values based on the set of super-clients. Each of the masked inputs is associated with a client from the plurality of clients and each of the aggregated random values is associated with a super-client from the set of super-clients. The method also includes aggregating the masked inputs from the plurality of clients and the aggregated random values from the set of super-clients.

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Heidelberg, Germany

Claudio Soriente