{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129573"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129573","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"ZKPerf: Performance benchmarking and Analysis of Zero-knowledge Frameworks and Virtual Machines","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Alluri, Lakshmi Saivenkata Siddhartha Varma"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kang, Daniel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-24","date_published":"2025-04-24","updated_at":"2026-07-22T22:25:05Z","subjects":["Cryptography","ZK-SNARKs","Benchmarking"],"languages":["en","eng"],"rights":["Copyright 2025 Lakshmi Saivenkata Siddhartha Varma Alluri"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129573","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kang, Daniel"]},{"key":"dc:creator","label":"Author","values":["Alluri, Lakshmi Saivenkata Siddhartha Varma"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-24","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cryptography","ZK-SNARKs","Benchmarking"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Lakshmi Saivenkata Siddhartha Varma Alluri"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129573"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Lakshmi Saivenkata Siddhartha Varma Alluri, accepted the attached license on 2025-04-23 at 23:14.","The student, Lakshmi Saivenkata Siddhartha Varma Alluri, submitted this Thesis for approval on 2025-04-23 at 23:25.","This Thesis was approved for publication on 2025-04-24 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21936 on 2025-10-19 at 19:16:07","Recent advancements in zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs) have led to the development of high-performing, accessible, and well-packaged tools for building ZK-SNARKs. This tooling enables developers to describe arbitrary computations from high-level definitions, supporting applications that range from ensuring the integrity of machine learning inferences to scaling blockchain transactions. Internally, these tools are built on various proof systems, commitment schemes, and arithmetization techniques, offering distinct performance characteristics that depend on the task. However, existing benchmarks are often granular and compare only a limited set of lower-level primitives and are limited in coverage of available tools. In this work, we present a comprehensive benchmark for evaluating the end-to-end performance of the ZK-SNARK tooling. We apply this benchmark across four popular frameworks and two virtual machines (VMs), comparing performance on four tasks: Elliptic Curve Digital Signature Algorithm (ECDSA) verification, Merkle Tree membership verification, an MNIST Convolutional Neural Network (CNN), and Meta's Deep Learning Recommendation Model (DLRM). Our results reveal that different provers excel in different areas, with the fastest prover for ECDSA proving MNIST CNN 6.7x slower than the fastest prover for MNIST CNN. We also investigate how implementing lower-level primitives such as Multi-Scalar Multiplications (MSMs) and Fast Fourier Transforms (FFTs) in the frameworks impacts the performance of the same protocol, observing a 7x difference in performance. Additionally, we observe up to a 10x variation in lookup performance across frameworks implementing the same lookup protocol as the number of queries increases for a fixed constant table size. We also compare the trade-offs between expressiveness and performance in ZK-SNARK frameworks and virtual machines (VMs). Finally, we open-source our implementation to serve as a foundation for the community in standardizing benchmarking for ZK-SNARK tooling."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["ZKPerf: Performance benchmarking and Analysis of Zero-knowledge Frameworks and Virtual Machines"]}]}],"canonical_facts":{"dc:contributor":["Kang, Daniel"],"dc:creator":["Alluri, Lakshmi Saivenkata Siddhartha Varma"],"dc:date":["2025-04-24","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Lakshmi Saivenkata Siddhartha Varma Alluri, accepted the attached license on 2025-04-23 at 23:14.","The student, Lakshmi Saivenkata Siddhartha Varma Alluri, submitted this Thesis for approval on 2025-04-23 at 23:25.","This Thesis was approved for publication on 2025-04-24 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21936 on 2025-10-19 at 19:16:07","Recent advancements in zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs) have led to the development of high-performing, accessible, and well-packaged tools for building ZK-SNARKs. This tooling enables developers to describe arbitrary computations from high-level definitions, supporting applications that range from ensuring the integrity of machine learning inferences to scaling blockchain transactions. Internally, these tools are built on various proof systems, commitment schemes, and arithmetization techniques, offering distinct performance characteristics that depend on the task. However, existing benchmarks are often granular and compare only a limited set of lower-level primitives and are limited in coverage of available tools. In this work, we present a comprehensive benchmark for evaluating the end-to-end performance of the ZK-SNARK tooling. We apply this benchmark across four popular frameworks and two virtual machines (VMs), comparing performance on four tasks: Elliptic Curve Digital Signature Algorithm (ECDSA) verification, Merkle Tree membership verification, an MNIST Convolutional Neural Network (CNN), and Meta's Deep Learning Recommendation Model (DLRM). Our results reveal that different provers excel in different areas, with the fastest prover for ECDSA proving MNIST CNN 6.7x slower than the fastest prover for MNIST CNN. We also investigate how implementing lower-level primitives such as Multi-Scalar Multiplications (MSMs) and Fast Fourier Transforms (FFTs) in the frameworks impacts the performance of the same protocol, observing a 7x difference in performance. Additionally, we observe up to a 10x variation in lookup performance across frameworks implementing the same lookup protocol as the number of queries increases for a fixed constant table size. We also compare the trade-offs between expressiveness and performance in ZK-SNARK frameworks and virtual machines (VMs). Finally, we open-source our implementation to serve as a foundation for the community in standardizing benchmarking for ZK-SNARK tooling."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129573"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Lakshmi Saivenkata Siddhartha Varma Alluri"],"dc:subject":["Cryptography","ZK-SNARKs","Benchmarking"],"dc:title":["ZKPerf: Performance benchmarking and Analysis of Zero-knowledge Frameworks and Virtual Machines"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}