research-article

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Zero Knowledge Static Program Analysis

Authors:

Joseph P. Near,

Yupeng ZhangAuthors Info & Claims

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Pages 2951 - 2967

https://doi.org/10.1145/3460120.3484795

Published: 13 November 2021 Publication History

Abstract

Static program analysis tools can automatically prove many useful properties of programs. However, using static analysis to prove to a third party that a program satisfies a property requires revealing the program's source code. We introduce the concept of zero-knowledge static analysis, in which the prover constructs a zero-knowledge proof about the outcome of the static analysis without revealing the program. We present novel zero-knowledge proof schemes for intra- and inter-procedural abstract interpretation. Our schemes are significantly more efficient than the naive translation of the corresponding static analysis algorithms using existing schemes. We evaluate our approach empirically on real and synthetic programs; with a pairing-based zero knowledge proof scheme as the backend, we are able to prove the control flow analysis on a 2,000-line program in 1,738s. The proof is only 128 bytes and the verification time is 1.4ms. With a transparent zero knowledge proof scheme based on discrete-log, we generate the proof for the tainting analysis on a 12,800-line program in 406 seconds, the proof size is 282 kilobytes, and the verification time is 66 seconds.

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Index Terms

Zero Knowledge Static Program Analysis
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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cover image ACM Conferences

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

November 2021

3558 pages

ISBN:9781450384544

DOI:10.1145/3460120

General Chairs:
Yongdae Kim
KAIST, Republic of Korea
,
Jong Kim
POSTECH, Republic of Korea
,
Program Chairs:
Giovanni Vigna
University of California, Santa Barbara / VMware, USA
,
Elaine Shi
Carnegie Mellon University, USA

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Published: 13 November 2021

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CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security

November 15 - 19, 2021

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Cited By

Wang ZMa PWang HWang S(2024)PP-CSA: Practical Privacy-Preserving Software Call Stack AnalysisProceedings of the ACM on Programming Languages10.1145/36498568:OOPSLA1(1264-1293)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649856
Deng HLiu TMa XWang WZhang CWu HZhu L(2024)Static program analysis for IoT risk mitigation in space-air-ground integrated networksSecurity and Safety10.1051/sands/20240073(2024007)Online publication date: 30-Apr-2024
https://doi.org/10.1051/sands/2024007
Cuéllar SHarris BParker JPernsteiner STromer ECalandrino JTroncoso C(2023)CheeseclothProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620602(6525-6540)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620602
Jiang KChait-Roth DDeStefano ZWalfish MWies T(2023)Less is more: refinement proofs for probabilistic proofs2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179393(1112-1129)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179393
Luo NAntonopoulos THarris WPiskac RTromer EWang XYin HStavrou ACremers CShi E(2022)Proving UNSAT in Zero KnowledgeProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3559373(2203-2217)Online publication date: 7-Nov-2022
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Yang YWang RWang YMiao XLiu BJiang S(2022)Simulink Model Static Analysis Results based on Abstract Interpretation2022 9th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA56465.2022.00087(605-614)Online publication date: Aug-2022
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