pith. sign in

arxiv: 1907.01489 · v1 · pith:ATVHLZMOnew · submitted 2019-07-02 · 💻 cs.CR

Secure Computation in Decentralized Data Markets

Fattaneh Bayatbabolghani , Bharath Ramsundar This is my paper

Pith reviewed 2026-05-25 10:56 UTC · model grok-4.3

classification 💻 cs.CR
keywords secure multi-party computationdecentralized data marketsgarbled circuitshomomorphic encryptionprivacy-preserving computationhealthcare applications
0
0 comments X

The pith

Decentralized data markets can enable secure computation on private contributor data using garbled circuits and homomorphic encryption.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper designs secure protocols for performing computations on data from decentralized markets while preserving privacy. It uses secure multi-party computation techniques, specifically garbled circuit evaluation and homomorphic encryption, to allow arbitrary computations without revealing individual data. The authors demonstrate the approach on healthcare applications to show practicality for sensitive datasets. This matters because it could let market stakeholders extract useful insights from combined data without compromising contributor privacy.

Core claim

Secure protocols utilizing garbled circuit evaluation and homomorphic encryption enable efficient and arbitrary secure computation in decentralized data markets, as shown through performance on healthcare domain applications.

What carries the argument

Garbled circuit evaluation combined with homomorphic encryption to realize secure multi-party computation protocols tailored for decentralized data market settings.

If this is right

  • Stakeholders in data markets can perform joint analyses on sensitive data without direct data sharing.
  • Healthcare insights can be derived from decentralized patient data while maintaining privacy.
  • Arbitrary computations become feasible in such markets beyond the reported applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such protocols might reduce the need for centralized data repositories in privacy-sensitive domains.
  • Performance on real healthcare datasets could guide scaling to other domains with similar privacy constraints.

Load-bearing premise

Standard garbled-circuit and homomorphic-encryption primitives can be composed into efficient, secure protocols for decentralized market settings without additional overheads or trust assumptions that would break practicality.

What would settle it

A demonstration that the composed protocols incur prohibitive computational costs or require new trust assumptions not present in the standard primitives when applied to decentralized markets.

Figures

Figures reproduced from arXiv: 1907.01489 by Bharath Ramsundar, Fattaneh Bayatbabolghani.

Figure 1
Figure 1. Figure 1: Designed protocol based on HE with steps corresponding to those in [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Designed protocol based on GC with indicated steps. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
read the original abstract

Decentralized data markets gather data from many contributors to create a joint data cooperative governed by market stakeholders. The ability to perform secure computation on decentralized data markets would allow for useful insights to be gained while respecting the privacy of data contributors. In this paper, we design secure protocols for such computation by utilizing secure multi-party computation techniques including garbled circuit evaluation and homomorphic encryption. Our proposed solutions are efficient and capable of performing arbitrary computation, but we report performance on two specific applications in the healthcare domain to emphasize the applicability of our methods to sensitive datasets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper claims to design secure protocols for computation over decentralized data markets by composing garbled-circuit evaluation and homomorphic encryption. It asserts that the resulting solutions are efficient, support arbitrary computation, and demonstrates applicability via performance results on two healthcare-domain applications involving sensitive datasets.

Significance. If the claimed protocols can be shown to be both secure and practically efficient without introducing prohibitive overhead or new trust assumptions, the work would enable privacy-preserving joint analysis in decentralized data cooperatives, a setting of growing importance for sensitive domains such as healthcare.

major comments (3)
  1. [Abstract] Abstract: the central claim that the solutions are 'efficient' and 'capable of performing arbitrary computation' is unsupported; the manuscript supplies neither a protocol description, a security definition or proof, nor any benchmark data or tables quantifying overhead.
  2. [Abstract / Introduction] The composition of standard garbled-circuit and homomorphic-encryption primitives is asserted to remain efficient in the decentralized market setting, yet no concrete construction, communication or computation complexity analysis, or discussion of additional trust assumptions appears.
  3. [Abstract] The two healthcare applications are said to illustrate performance, but no experimental setup, baseline comparisons, or quantitative results (runtime, communication, accuracy) are provided to substantiate the efficiency claim.
minor comments (1)
  1. [Abstract] The abstract refers to 'market stakeholders' and 'data cooperative' without defining the precise trust or incentive model assumed for the decentralized setting.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the current manuscript does not include protocol descriptions, security definitions or proofs, complexity analyses, trust assumption discussions, or experimental results to support the claims in the abstract. We will revise the paper accordingly to address these gaps.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the solutions are 'efficient' and 'capable of performing arbitrary computation' is unsupported; the manuscript supplies neither a protocol description, a security definition or proof, nor any benchmark data or tables quantifying overhead.

    Authors: We acknowledge that the manuscript as submitted lacks these supporting elements. In the revised version we will add a dedicated section with the protocol description, security definitions and proofs, and benchmark tables quantifying overhead. revision: yes

  2. Referee: [Abstract / Introduction] The composition of standard garbled-circuit and homomorphic-encryption primitives is asserted to remain efficient in the decentralized market setting, yet no concrete construction, communication or computation complexity analysis, or discussion of additional trust assumptions appears.

    Authors: We agree no concrete construction or analysis is present. The revision will include the explicit construction, communication and computation complexity analysis, and discussion of any additional trust assumptions. revision: yes

  3. Referee: [Abstract] The two healthcare applications are said to illustrate performance, but no experimental setup, baseline comparisons, or quantitative results (runtime, communication, accuracy) are provided to substantiate the efficiency claim.

    Authors: The manuscript currently omits these details. We will add a full experimental section describing the setup, baselines, and quantitative results including runtime, communication, and accuracy for the two applications. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a protocol-design contribution that composes standard garbled-circuit and homomorphic-encryption primitives for decentralized markets and evaluates them on two healthcare applications. No equations, fitted parameters, self-definitional steps, or load-bearing self-citations appear in the abstract or described construction. The central claim is an engineering composition whose correctness rests on external, independently verified primitives rather than any reduction to the paper's own inputs or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard cryptographic assumptions for the security of garbled circuits and homomorphic encryption; no new entities or fitted parameters are introduced in the abstract.

axioms (1)
  • domain assumption Standard security assumptions of secure multi-party computation (semi-honest or malicious adversaries, honest majority, etc.) hold for the chosen primitives.
    Implicit in any claim that garbled-circuit evaluation and homomorphic encryption yield secure protocols.

pith-pipeline@v0.9.0 · 5612 in / 1208 out tokens · 26519 ms · 2026-05-25T10:56:17.119946+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 2 internal anchors

  1. [1]

    available at: Ucimachine learning reposi- tory

    Breast cancer wisconsin dataset. available at: Ucimachine learning reposi- tory

    work page

  2. [2]

    Abadi, P

    M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. Tensorflow: A system for large- scale machine learning. In 12th{USENIX} Symposium on Operating Sys- tems Design and Implementation ( {OSDI} 16), pages 265–283, 2016

    work page 2016

  3. [3]

    Secure Fingerprint Alignment and Matching Protocols

    F. Bayatbabolghani, M. Blanton, M. Aliasgari, and M. Goodrich. Se- cure fingerprint alignment and matching protocols. arXiv preprint arXiv:1702.03379, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  4. [4]

    Bellare, V

    M. Bellare, V. Hoang, S. Keelveedhi, and P. Rogaway. The JustGarble library. http://cseweb.ucsd.edu/groups/justgarble/. 11 range garbling evaluation #gates #non-XOR gates Comm. 10 8.2 4.8 198909 106016 5.1 11 14.6 8.7 318717 193056 9.3 12 28.6 17.0 562429 371232 17.8 Table 3: Execution time for LR test in ms and the communication in MB for GC

    work page

  5. [5]

    Bellare, V

    M. Bellare, V. Hoang, S. Keelveedhi, and P. Rogaway. Efficient garbling from a fixed-key blockcipher. In IEEE Symposium of Security and Privacy , pages 478–492, 2013

    work page 2013

  6. [6]

    R. Chen, B. Ramsundar, and R. Robbins. Fair value and decentralized governance of data. https://github.com/computablelabs/computable/ blob/master/whitepaper\/computable_whitepaper.pdf, 2019

    work page 2019

  7. [7]

    Groce, A

    A. Groce, A. Ledger, A. J. Malozemoff, and A. Yerukhimovich. Compgc: Efficient offline/online semi-honest two-party computation. IACR Cryptol- ogy ePrint Archive , 2016:458, 2016

    work page 2016

  8. [8]

    D. G. Kleinbaum, K. Dietz, M. Gail, and M. Klein. Logistic regression. Springer, 2002

    work page 2002

  9. [9]

    Kolesnikov and T

    V. Kolesnikov and T. Schneider. Improved garbled circuit: Free XOR gates and applications. In International Colloquium on Automata, Languages and Programming, pages 486–498, 2008

    work page 2008

  10. [10]

    Paszke, S

    A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in py- torch. 2017

    work page 2017

  11. [11]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit- learn: Machine learning in python. Journal of machine learning research , 12(Oct):2825–2830, 2011

    work page 2011

  12. [12]

    J. K. Pritchard and M. Przeworski. Linkage disequilibrium in humans: models and data. The American Journal of Human Genetics , 69(1):1–14, 2001

    work page 2001

  13. [13]

    Tokenized Data Markets

    B. Ramsundar, R. Chen, A. Vasudev, R. Robbins, and A. Gorokh. Tok- enized data markets. arXiv:1806.00139, 2019

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  14. [14]

    http://sealcrypto

    Simple Encrypted Arithmetic Library (release 3.0.0). http://sealcrypto. org, Oct. 2018. Microsoft Research, Redmond, WA

    work page 2018

  15. [15]

    Shahbazi, F

    A. Shahbazi, F. Bayatbabolghani, and M. Blanton. Private computation with genomic data for genome-wide association and linkage studies. In In- ternational Workshop on Genomic Privacy and Security (GenoPri) , 2016. 12

    work page 2016

  16. [16]

    E. M. Songhori, S. U. Hussain, A. R. Sadeghi, T. Schneider, and F. Koushanfar. Tinygarble: Highly compressed and scalable sequential garbled circuits. In Security and Privacy (SP), 2015 IEEE Symposium on , pages 411–428. IEEE, 2015

    work page 2015

  17. [17]

    A. C. Yao. Protocols for secure computations. In FOCS, volume 82, pages 160–164, 1982

    work page 1982

  18. [18]

    A. C. Yao. How to generate and exchange secrets. In 27th Annual Sym- posium on Foundations of Computer Science (sfcs 1986) , pages 162–167. IEEE, 1986

    work page 1986

  19. [19]

    Zahur, M

    S. Zahur, M. Rosulek, and D. Evans. Two halves make a whole: Re- ducing data transfer in garbled circuits using half gates. In International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT), pages 220–250, 2015. 13

    work page 2015