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Zero Knowledge Proofs for Decision Tree Predictions and Accuracy

Authors:

Dawn SongAuthors Info & Claims

CCS '20: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Pages 2039 - 2053

https://doi.org/10.1145/3372297.3417278

Published: 02 November 2020 Publication History

Abstract

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning models that are claimed to achieve high accuracy remains challenging, and the correctness and consistency of machine learning predictions in real products lack any security guarantees. In this paper, we initiate the study of zero knowledge machine learning and propose protocols for zero knowledge decision tree predictions and accuracy tests. The protocols allow the owner of a decision tree model to convince others that the model computes a prediction on a data sample, or achieves a certain accuracy on a public dataset, without leaking any information about the model itself. We develop approaches to efficiently turn decision tree predictions and accuracy into statements of zero knowledge proofs. We implement our protocols and demonstrate their efficiency in practice. For a decision tree model with 23 levels and 1,029 nodes, it only takes 250 seconds to generate a zero knowledge proof proving that the model achieves high accuracy on a dataset of 5,000 samples and 54 attributes, and the proof size is around 287 kilobytes.

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Wang ZGai K(2024)Decision Tree-Based Federated Learning: A SurveyBlockchains10.3390/blockchains20100032:1(40-60)Online publication date: 7-Mar-2024
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Pappas CPapadopoulos DLuo BLiao XXu JKirda ELie D(2024)Sparrow: Space-Efficient zkSNARK for Data-Parallel Circuits and Applications to Zero-Knowledge Decision TreesProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690318(3110-3124)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690318
Sun HLi JZhang HLuo BLiao XXu JKirda ELie D(2024)zkLLM: Zero Knowledge Proofs for Large Language ModelsProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670334(4405-4419)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3670334
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Index Terms

Zero Knowledge Proofs for Decision Tree Predictions and Accuracy
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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cover image ACM Conferences

CCS '20: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

October 2020

2180 pages

ISBN:9781450370899

DOI:10.1145/3372297

General Chairs:
Jay Ligatti
University of South Florida, USA
,
Xinming Ou
University of South Florida, USA
,
Program Chairs:
Jonathan Katz
University of Maryland, USA
,
Giovanni Vigna
University of California-Santa Barbara, USA

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2020

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Defense Advanced Research Projects Agency
DARPA
National Science Foundation(NSF)

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CCS '20

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CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security

November 9 - 13, 2020

Virtual Event, USA

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Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Wang ZGai K(2024)Decision Tree-Based Federated Learning: A SurveyBlockchains10.3390/blockchains20100032:1(40-60)Online publication date: 7-Mar-2024
https://doi.org/10.3390/blockchains2010003
Pappas CPapadopoulos DLuo BLiao XXu JKirda ELie D(2024)Sparrow: Space-Efficient zkSNARK for Data-Parallel Circuits and Applications to Zero-Knowledge Decision TreesProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690318(3110-3124)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690318
Sun HLi JZhang HLuo BLiao XXu JKirda ELie D(2024)zkLLM: Zero Knowledge Proofs for Large Language ModelsProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670334(4405-4419)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3670334
Daftardar AReagen BGarg S(2024)SZKP: A Scalable Accelerator Architecture for Zero-Knowledge ProofsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676898(271-283)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3656019.3676898
Qiu PWu GChu TWei CLuo RYan YWang WZhang HDe V(2024)MSMAC: Accelerating Multi-Scalar Multiplication for Zero-Knowledge ProofProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655672(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655672
Kumar AAguilera MTourani RMisra SQuek TGao DZhou JCardenas A(2024)A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3657015(1246-1260)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3657015
Qin HHe DFeng QKhan MLuo MChoo K(2024)Cryptographic Primitives in Privacy-Preserving Machine Learning: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332180336:5(1919-1934)Online publication date: May-2024
https://doi.org/10.1109/TKDE.2023.3321803
Fan YMa KZhang LLei XXu GTan G(2024)ValidCNN: A Large-Scale CNN Predictive Integrity Verification Scheme Based on zk-SNARKIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337164321:6(5185-5195)Online publication date: Nov-2024
https://doi.org/10.1109/TDSC.2024.3371643
Duan HPeng ZXiang LHu YLi B(2024)A Verifiable and Privacy-Preserving Federated Learning Training FrameworkIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3369658(1-14)Online publication date: 2024
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Hou JLiu DHuang CZhuang WShen XSun RYing B(2024)Data Protection: Privacy-Preserving Data Collection With ValidationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.332629921:4(3422-3438)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3326299
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