Secure Computation on the Web: Computing without Simultaneous Interaction

By Shai Halevi, Yehuda Lindell, Benny Pinkas. 2011

147 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Privacy & Security

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “There is a rich literature exploring secure aggregation in both the single-server setting (via pairwise additive masking [12, 188, 73], via threshold homomorphic encryption [356, 193, 92], and via generic secure multi-party computation [86]) as well as in the multiple non-colluding servers setting [”
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “While there have been both theoretical and appliedworks proposing secure computation solutions for settings with restricted communication [10, 14, 22, 26, 30, 33], Bonawitz et al.”
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “Homomorphic encryption is a cryptographic secure computation scheme that allows aggregations to be performed on encrypted data [22]–[24].”
Secure Aggregation with Heterogeneous Quantization in Federated Learning (2020, background): “Second, local models can be securely aggregated at the central server to update the global model [1], [13]–[18].”
Server-Aided Secure Computation with Off-line Parties (2017, background): “Indeed, [27] shows that a collusion between server and any user Uj allows them to learn the residual function on many inputs of their choice.”
Secure Multiparty Computation from SGX (2017, background): “Furthermore, the protocol is non-interactive in the sense that each user can perform an initial set-up, and then provide its inputs and receive outputs independently of other protocol participants, which means that it provides a solution for “secure computation on the web” [24] with standard MPC sec”
Multiparty Computation with Low Communication, Computation and Interaction via Threshold FHE (2012, background): “Halevi, Lindell and Pinkas [21] recently considered the model of " secure computation on the web " which gets rid of all interaction between the actual parties, and instead only allows each party to " log in " once to interact with the server.”
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, background): “Halevi, Lindell and Pinkas [36] present a protocol that uses homomorphic encryption to securely compute the sum in just one round of interaction between the server and each of the clients (assuming a PKI is already in place).”