Zero-Knowledge Proofs on Secret-Shared Data via Fully Linear PCPs

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Advances and Open Problems in Federated Learning (2019, background): “Of interest in this setting, recent work enables to handle the case where the multiple verifiers have shares of the statement [76].”
Transparent SNARKs from DARK Compilers (2020, methodology): “We deﬁne a Polynomial IOP as a reﬁnement of algebraic linear IOPs [38,9,11], where in each round of interaction the prover provides the veriﬁer with oracle access to a multivariate polynomial function of bounded degree.”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, background): “Recent works [43, 44] explore a stronger adversarial model called guaranteed output delivery in a similar 3 party honest majority corruption model.”
Spartan: Efficient and general-purpose zkSNARKs without trusted setup (2020, methodology): “We are motivated to design zkSNARKs because they enable many applications that involve various forms of delegation of computation for scalability or privacy [17, 33, 36, 38, 48, 49, 51, 56, 74, 76, 88, 91–96, 98, 108].”