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Fully homomorphic encryption for machine learning

Abstract : Fully homomorphic encryption enables computation on encrypted data without leaking any information about the underlying data. In short, a party can encrypt some input data, while another party, that does not have access to the decryption key, can blindly perform some computation on this encrypted input. The final result is also encrypted, and it can be recovered only by the party that possesses the secret key. In this thesis, we present new techniques/designs for FHE that are motivated by applications to machine learning, with a particular attention to the problem of homomorphic inference, i.e., the evaluation of already trained cognitive models on encrypted data. First, we propose a novel FHE scheme that is tailored to evaluating neural networks on encrypted inputs. Our scheme achieves complexity that is essentially independent of the number of layers in the network, whereas the efficiency of previously proposed schemes strongly depends on the topology of the network. Second, we present a new technique for achieving circuit privacy for FHE. This allows us to hide the computation that is performed on the encrypted data, as is necessary to protect proprietary machine learning algorithms. Our mechanism incurs very small computational overhead while keeping the same security parameters. Together, these results strengthen the foundations of efficient FHE for machine learning, and pave the way towards practical privacy-preserving deep learning. Finally, we present and implement a protocol based on homomorphic encryption for the problem of private information retrieval, i.e., the scenario where a party wants to query a database held by another party without revealing the query itself.
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  • HAL Id : tel-01918263, version 2

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Michele Minelli. Fully homomorphic encryption for machine learning. Cryptography and Security [cs.CR]. Université Paris sciences et lettres, 2018. English. ⟨NNT : 2018PSLEE056⟩. ⟨tel-01918263v2⟩

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