2026-09-18T21:21:24Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/130186 2026-02-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01 The student, Amit Agarwal, accepted the attached license on 2025-07-14 at 16:26. The student, Amit Agarwal, submitted this Dissertation for approval on 2025-07-14 at 16:48. This Dissertation was approved for publication on 2025-07-16 at 20:49. DSpace SAF Submission Ingestion Package generated from Vireo submission #22561 on 2025-10-25 at 15:53:56 Theoretical and practical advances in preprocessing based secure computation Agarwal, Amit 2025-07-16 Khurana, Dakshita Khurana, Dakshita Miller, Andrew Gunter, Carl Beaver, Donald Secure Computation Zero-knowledge Proofs Cryptography eng Secure computation — often called multiparty computation (MPC) — is a cornerstone of modern cryptography, enabling multiple parties to jointly compute functions over their private inputs without revealing those inputs. Over the past two decades, the preprocessing model of MPC has emerged as a powerful paradigm for improving practical efficiency. In this approach, the protocol is split into two distinct phases: 1.) Offline (input-independent) phase: Parties perform the bulk of the cryptographic work ahead of time to generate “correlated randomness.” 2.) Online (input-dependent) phase: Parties consume that precomputed “correlated randomness” to execute the actual secure computation task in an efficient way. By shifting intensive computations to the offline phase, the online phase can run with minimal latency, significantly reducing the response time of the protocol. This dissertation tackles the two core challenges of preprocessing-based MPC: 1.) Choosing the right correlated randomness: We study two important applications — secure sorting and secure logistic regression — and identify specialized forms of correlated randomness that yield communication-efficient online protocols for each task. 2.) Generating and storing correlated randomness efficiently: We introduce new techniques for producing two key types of correlations — unit-vector correlations and doubly-authenticated bits — by harnessing pseudorandom generators with enhanced properties. We further demonstrate how these correlations accelerate secure computation and zero-knowledge proofs respectively. Together, these contributions advance the state of the art in MPC by both broadening the range of efficiently. solvable tasks and streamlining the resources required to prepare for them. 2025-08 Text https://hdl.handle.net/2142/130186 Copyright 2025 Amit Agarwal Computer Science Computer Science University of Illinois Urbana-Champaign Ph.D. Dissertation