On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Advances and Open Problems in Federated Learning (2019, background): “Thanks to both theoretical and engineering breakthroughs, the field has moved from being of a purely theoretical interest to a deployed technology in industry [71, 70, 257, 29, 169, 209, 210].”
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “Once considered a purely theoretical tool, cryptographic secure multiparty computation has become a tool that underlies several technological solutions [1, 7, 8, 12, 23, 27, 29].”
CrypTFlow: Secure TensorFlow Inference (2019, background): “[49], [58], [7]), and MPC protocols for other applications [52], [47].”
Scalable Private Set Union from Symmetric-Key Techniques (2019, background): “Over the last decade, there has been a signiﬁcant amount of work on private set intersection [15, 16, 3, 29, 18, 46, 47, 36, 12, 37, 51, 11, 27, 49, 10, 20, 13, 17, 45, 43, 32].”
Private Join and Compute from PIR with Default (2020, methodology): “The ﬁrst application involves privacy-preserving computation for the eﬀectiveness of advertising campaigns, which is a generalization of the functionality supported by Ion et al. [IKN + 20].”