AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach

Al Amin; Kamrul Hasan; Liang Hong; Sharif Ullah

REVIEW 5 major objections 5 minor 20 references

AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a Vision Transformer trained on medical images encrypted by a key-dependent block-pixel scrambling scheme reaches 94% validation accuracy and resists the tested noise and reconstruction attacks.

desk verdict Plausible applied result for ViT on encrypted medical images, but the confidentiality guarantee is asserted rather than demonstrated and needs a real threat model. read the letter →

arxiv 2501.15363 v1 pith:3LQGI3RQ submitted 2025-01-26 cs.CR cs.CV

Al Amin , Kamrul Hasan , Sharif Ullah , Liang Hong This is my paper · ORCID

classification cs.CRcs.CV

keywords Privacy-preservingdatasharingLearnableencryptionBlock-pixeloperationVisionTransformerMedicalimageclassificationAdversarialrobustnessintegrityConfidentiality

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to show that medical images can be encrypted with a simple key-dependent block-pixel scrambling scheme and still be classified accurately by a Vision Transformer, without needing to decrypt them. It reports 95% training and 94% validation accuracy on encrypted MRI brain tumor and lung/colon histopathology images, close to the plain-image accuracy of standard CNNs, and 84–90% validation accuracy when 10–20% of the encrypted training set is corrupted by bit-flip or Gaussian noise. If true, this would make cloud-based diagnostic services feasible on sensitive medical data while keeping the images encrypted end to end. The paper also claims that the two reconstruction attacks it tried, Leading Bit and Minimum Difference, do not recover recognizable images, supporting the confidentiality side of the proposal.

What carries the argument

The load-bearing mechanism is the block-pixel learnable encryption pipeline followed by Vision Transformer processing. An input image is divided into 64 patches of size $25 \times 25 \times 3$; each patch's pixels are permuted under key $K$, patch positions are shuffled by a key-driven permutation $\sigma$, pixel values are conditionally inverted via $x' = 255 - x$, and RGB channels are permuted, after which the patches are reassembled into the encrypted image. The encrypted image is then embedded as 768-dimensional patch vectors with positional encodings and processed by transformer encoder layers of multi-head self-attention and MLPs, with a classification token read out by a softmax head. The encryption hides visual content per client key while preserving enough structure for the transformer to learn classification.

What would settle it

Give an adversary one plaintext–ciphertext pair produced under a single client's key and check whether they can recover the key-driven permutation of pixels, patch shuffle, inversion mask, and channel shuffle; if they can, every other image encrypted under that key is decryptable and the confidentiality claim fails. A weaker test is to run a stronger reconstruction attack than the two demonstrated, such as a learned inversion network trained on the encrypted images, and see whether recognizable or class-revealing images emerge.

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Extended reading notes

Core claim

The central claim is that the combination of a learnable block-pixel encryption and a Vision Transformer forms a workable and efficient privacy-preserving pipeline for medical image classification. On encrypted MRI brain tumor and lung/colon histopathological datasets, the pipeline reaches 95% training and 94% validation accuracy, close to the plain-image accuracy of the standard CNN baselines, while ResNet50, MobileNetV2, and InceptionV3 drop to 38–51% on the same encrypted inputs. Under integrity attacks, with 10% and 20% bit-flip and Gaussian noise injected into the encrypted training data, validation accuracy stays between 84% and 90%. The paper further reports that Leading Bit and Minimum Difference reconstruction attacks fail to recover recognizable images, on the stated assumption that the adversary knows the encryption algorithm but not the client's key.

Load-bearing premise

The privacy claim depends on an adversary knowing the encryption algorithm but not the client's key; the paper asserts, rather than proves, that without the key the scrambling, inversion, and channel shuffles cannot be reversed or used to infer the original medical image.

Editorial extensions

If this is right

Medical images can be classified in the encrypted domain at near-plaintext accuracy, so cloud services could run diagnostic models without ever decrypting the data.
The framework keeps validation accuracy between 84% and 90% when 10% or 20% of the encrypted training data is corrupted by bit-flip or Gaussian noise, indicating tolerance to integrity attacks.
Standard CNNs fall to 38–51% accuracy on encrypted images while the ViT-based approach reaches 94%, suggesting transformer architectures are better suited to this encrypted representation.
Training the proposed model takes about 82 minutes versus 185–542 minutes for the CNNs on encrypted data in the reported setup, so the approach claims both accuracy and computational efficiency.
The two reconstruction attacks tested, Leading Bit and Minimum Difference, do not recover recognizable images from the encrypted data, supporting the confidentiality claim under the stated key-unknown adversary model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The security result is only as strong as key secrecy: the encryption is a deterministic keyed permutation pipeline, so any leak or reuse of a client's key makes the transformations invertible and the confidentiality claim void.
Because the same key is used across many images for a client, a known-plaintext attack with one original/encrypted pair could in principle recover the per-key permutation, patch shuffle, inversion mask, and channel shuffle; testing this would define the real security boundary.
The reported robustness may partly reflect ViT's general tolerance to input corruption rather than properties of this specific encryption; comparing a plain-image ViT under identical noise would isolate the encryption's contribution.
The patch size and image size are tied to the medical datasets used, so whether the accuracy gain over CNNs survives on larger or higher-resolution images is an open, testable question.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a reasonable applied result — a Vision Transformer trained on block-pixel encrypted medical images reaches 94% validation accuracy on brain MRI and lung/colon histopathology. The integrity experiments (Gaussian noise, bit-flip at 10/20%) are simple but honestly reported. What is not established is the confidentiality claim, and that is the paper's central selling point.

The genuinely new bit is the combination: multi-step block-pixel encryption (patch scrambling, patch shuffle, pixel inversion, channel shuffle) with ViT, plus adversarial tests on the encrypted training data. Prior work used DNNs with block-wise encryption; showing ViT handles this better (95% vs 38-51% on encrypted MRI) is a useful data point, even though the comparison uses only plain pixel-shuffling for the baselines rather than the full proposed encryption.

The soft spots are concentrated in Section IV-D. The privacy argument is: an attacker who knows the encryption algorithm but not the key cannot reconstruct the image. The only evidence is two heuristic attacks (Leading Bit, Minimum Difference) shown as example images in Figure 4, with no PSNR/SSIM, no baseline, and no threat model. The stress-test concern is on point: the encryption is deterministic and key reuse per client means a single known plaintext-ciphertext pair could plausibly reveal the per-patch permutation, inversion mask, and channel shuffle, after which every other image from that client decrypts. The paper never discusses known-plaintext adversaries. It also preserves the multiset of pixel values and small-patch statistics, so a statistical reconstruction attack with a medical-image prior is not addressed. The claim that reconstruction is 'infeasible' is therefore unsupported.

The empirical side is also under-supported: no error bars or repeated runs, no hyperparameters, no code. The 94% figure is plausible but not pinned down.

Who this is for: someone working on privacy-preserving medical image classification who wants a concrete ViT baseline and a reminder that adversarial robustness needs quantitative evaluation. It is not a trustworthy security analysis. That said, the paper is coherent and the experiments are real, so it deserves a serious referee — I would send it to peer review with the expectation of major revision, asking for a proper threat model, known-plaintext analysis, quantitative reconstruction metrics, and code/data release.

Referee Report

5 major / 5 minor

Summary. The paper proposes a framework for privacy-preserving medical image classification that combines a keyed block-pixel learnable encryption scheme with a Vision Transformer (ViT) classifier. Each client encrypts local images using a client-specific key via patch scrambling, patch shuffling, negative-positive inversion, and channel shuffling; the encrypted images are then classified by a ViT on a central server. The authors report 95% training and 94% validation accuracy on encrypted MRI brain tumor and lung/colon histopathology datasets, plus robustness to Gaussian noise and bit-flip integrity attacks, and resistance to two heuristic reconstruction attacks (Leading Bit and Minimum Difference). The core claims are that the framework preserves classification accuracy in the encrypted domain and provides confidentiality of medical images under key secrecy.

Significance. If the confidentiality and robustness claims were rigorously established, the framework would be a useful practical contribution to privacy-preserving medical image analysis, particularly because it avoids heavy cryptographic primitives and claims computational efficiency. The combination of a transformer with block-pixel encryption is a plausible design direction, and the reported accuracy on encrypted medical images is higher than typical baselines in this area. However, the significance is currently limited by the absence of a formal or even a quantitatively rigorous security evaluation: the confidentiality claim rests on two qualitative attack demonstrations, and the empirical accuracy results are reported without variance, hyperparameters, or a fair baseline comparison. The paper's value proposition—"trustworthy and privacy-preserving"—is therefore not yet supported to the standard expected for a security-adjacent venue.

major comments (5)

[Section IV-D, Confidentiality Evaluation] The central confidentiality claim is not substantiated. The only evidence is Figure 4, which shows example images after the Leading Bit and Minimum Difference attacks, with no quantitative metrics (e.g., PSNR, SSIM, or classification of reconstructed images) and no comparison to a baseline or to attacks on plaintext images. The sentence "preventing the reconstruction of original images and demonstrating its robustness" is a visual assertion, not a security evaluation. The paper needs a defined threat model (adversary capabilities, access to ciphertexts, known/plaintext knowledge) and quantitative reconstruction-resistance measures, ideally including attacks that exploit the structure of the scheme.
[Section III-A, Eqs. (2)-(6)] The encryption scheme is a deterministic keyed permutation and inversion that preserves the multiset of pixel values and operates on small 25×25 patches. Because each client reuses the same key for all of their images, a known-plaintext attack on a single image would in principle reveal the per-patch permutation, inversion mask, and channel shuffle, enabling decryption of every other image encrypted with that key. The paper neither discusses this adversary nor provides any argument that the scheme resists it. Since Section IV-D explicitly assumes the adversary knows the encryption algorithm but not the key, the known-plaintext scenario is within the stated threat model and must be addressed.
[Table II and Section IV-C] The comparison against DNN baselines is not controlled. The DNN models are evaluated under "Pixel Shuffling" encryption while the proposed framework uses the full block-pixel encryption; the table therefore conflates the effect of the ViT architecture with the effect of the encryption method. To support the claim that the proposed framework outperforms traditional DNNs in the encrypted domain, the baselines must be trained under the same learnable encryption scheme, or at least the comparison must be reported as a joint architecture-plus-encryption evaluation rather than a head-to-head of the classifiers.
[Section IV-A and Table I] All empirical accuracy figures are reported without error bars, repeated runs, or hyperparameter settings (learning rate, batch size, number of epochs, optimizer, patch embedding dimension). The 94% validation accuracy may be a single run, so it is not possible to assess the stability of the result or whether the confidence intervals overlap with the DNN baselines. The paper should report mean and standard deviation over multiple random seeds and list the training hyperparameters.
[Section IV-D, Integrity Evaluation] The integrity evaluation is limited to 10% and 20% perturbation of training data, with the statement that "strategic attackers don't poison the full data as too much perturbation generates more correlated alerts." This is an unproven assumption about attacker behavior. A stronger adversary could poison a larger fraction or target the validation/test distribution, and the robustness claim should be bounded by the attack fraction actually tested. Please either test stronger perturbations or explicitly state the attack model under which the 85-90% accuracy holds.

minor comments (5)

[Section III-A, Eq. (1)] Eq. (1) writes "64 = 200×200 / 252", which appears to be a typesetting error for 25^2 (the patch area). Please correct the notation to avoid confusion.
[Section III-A, Eq. (4)] Eq. (4) contains "x&if r = 0", which should read "x if r = 0". The ampersand is a typographical artifact.
[Abstract and Section IV-D] The manuscript repeatedly uses "it's" where "its" is meant (e.g., "it’s robustness" in Section IV-D, "it’s capability" in the abstract). Please proofread for possessive forms.
[Section IV-B] The dataset description states "5,712 MRI images (with 1,311 used for testing)" and "2,980 histopathological images (with 311 for testing)". Please clarify whether these are after train/test split and whether the splits are stratified, so that the reported accuracies are interpretable.
[Section II-B] The related-work section discusses prior learnable encryption schemes but does not cite several closely related ViT-based encrypted-domain works (e.g., Kiya et al. is cited as [8], but the discussion of block-wise encryption for ViTs would benefit from a more direct comparison with recent transformer-based privacy-preserving methods). Please expand the comparison to position the contribution more precisely.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the accuracy and robustness results are empirical and the encryption/classification pipeline is self-contained.

full rationale

The paper's central claims are empirical: the 94% validation accuracy is obtained by training a Vision Transformer end-to-end on images encrypted with the block-pixel scheme defined in Eqs. (1)-(6), and the integrity results in Table I are measured under noise perturbations. No parameter is fitted to a subset of data and then reported as a prediction of a closely related quantity. The confidentiality evaluation tests two reconstruction heuristics (Leading Bit and Minimum Difference) cited from prior work [9]; their failure on example images is reported as an empirical observation rather than derived from the encryption definition. Although this is weak evidence for the broad confidentiality claim (the key-secrecy assumption is asserted, not proven, and no formal security argument is given), that is a soundness/evidence limitation, not a circular reduction. There are no load-bearing self-citations, no uniqueness theorem imported from the authors' prior work, and no known result renamed as new. The derivation chain is therefore self-contained with respect to the accuracy claims; the security claim is under-supported but not circular.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on two unevidenced assumptions: that scrambling preserves classification-relevant features, and that an attacker without the key cannot reconstruct the data. The free parameters are standard or hand-chosen hyperparameters and experiment settings, not values fitted to the target accuracy. No new physical or mathematical entities are introduced.

free parameters (3)

Patch size = 25 x 25
Hand-chosen to divide 200x200 images into 64 patches; no security or performance justification is given in Section III-A.
Embedding dimension = 768
Standard ViT-Base dimension used without tuning; other model hyperparameters such as layers, heads, and optimizer are not reported in Section III-B.
Noise perturbation levels = 10% and 20%
Chosen for Bit-Flip and Gaussian Noise attacks in Table I; no sensitivity analysis is presented.

assumptions (3)

domain assumption The block-pixel encryption operations preserve enough discriminative information for the Vision Transformer to classify images accurately.
This is the basis for the 94% validation accuracy claim; it is empirically observed on two datasets but not proven. It enters in Section III-A and is supported only by results in Table II.
domain assumption An adversary who knows the encryption method but not the client's key cannot reconstruct or meaningfully infer the original image.
Stated as the threat model in Section IV-D and relied on for the confidentiality claim; no formal security proof is provided.
domain assumption The four tested attacks, Bit-Flip, Gaussian Noise, Leading Bit, and Minimum Difference, are representative of realistic adversarial threats.
The robustness and confidentiality claims are based solely on these attacks, described in Sections III-D and III-E; the paper does not analyze other attack families.

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Cite this review

Pith. "Pith review of AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach." pith.science (2026). https://pith.science/paper/3LQGI3RQ

@misc{pith2026250115363,
  author       = {Pith},
  title        = {Pith review of: AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3LQGI3RQ}},
  note         = {Machine review of arXiv:2501.15363}
}

read the original abstract

In the era of data-driven decision-making, ensuring the privacy and security of shared data is paramount across various domains. Applying existing deep neural networks (DNNs) to encrypted data is critical and often compromises performance, security, and computational overhead. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on the block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against adversarial attacks without compromising computational efficiency and data integrity. The framework was tested on sensitive medical datasets to validate its efficacy, proving its ability to handle highly confidential information securely. The suggested framework was validated with a 94\% success rate after extensive testing on real-world datasets, such as MRI brain tumors and histological scans of lung and colon cancers. Additionally, the framework was tested under diverse adversarial attempts against secure data sharing with optimum performance and demonstrated its effectiveness in various threat scenarios. These comprehensive analyses underscore its robustness, making it a trustworthy solution for secure data sharing in critical applications.

Figures

Figures reproduced from arXiv: 2501.15363 by the authors.

**Figure 2.** Original image and encrypted images generated using [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Training and Validation Accuracy and Loss Curves for [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Transformation Pipeline: Original, Encrypted, and Post [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Privacy-aware mobile sensing in vehicular networks,

M. Guo, N. Pissinou, and S. Iyengar, “Privacy-aware mobile sensing in vehicular networks,” in 2016 International Conference on Computing, Networking and Communications (ICNC) , pp. 1–5, 2016

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X. Zhao, Z. Qi, S. Wang, Q. Wang, X. Wu, Y . Mao, and L. Zhang, “Rcps: Rectified contrastive pseudo supervision for semi-supervised medical image segmentation,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 1, pp. 251–261, 2024

work page 2024

[3] [3]

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K. Madono, M. Tanaka, M. Onishi, and T. Ogawa, “Block- wise scrambled image recognition using adaptation network,” CoRR, vol. abs/2001.07761, 2020

work page Pith review arXiv 2001

[4] [4]

Hensel’s compression- based dimensionality reduction approach for privacy protection in federated learning,

A. E. Ouadrhiri, A. Abdelhadi, and P. H. Phung, “Hensel’s compression- based dimensionality reduction approach for privacy protection in federated learning,” in 2023 International Conference on Computing, Networking and Communications (ICNC) , pp. 298–303, 2023

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Research on privacy protection scheme for educational data based on blockchain,

X. Gao, “Research on privacy protection scheme for educational data based on blockchain,” in 2023 3rd International Conference on Com- puter Science and Blockchain (CCSB) , pp. 205–208, 2023

work page 2023

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“Health information privacy.” https://www.hhs.gov/hipaa/ for-professionals/privacy/index.html#:∼:text=The%20HIPAA% 20Privacy%20Rule%20establishes,care%20providers%20that% 20conduct%20certain, 2024

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H. Kiya, R. Iijima, and T. Nagamori, “Block-wise encryption for reliable vision transformer models,” 2023

work page 2023