Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications

Baraq Ghaleb; Faneela; Jawad Ahmad; Sana Ullah Jan; William J. Buchanan

REVIEW 4 major objections 4 minor 23 references

Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A credit-card approval classifier can run fully encrypted and still match plaintext accuracy within half a percentage point.

desk verdict The paper's central claim of a fully encrypted SVM inference pipeline is unsupported because the RBF kernel evaluation is left unspecified and the adaptive threshold requires plaintext scores. read the letter →

arxiv 2505.05920 v1 pith:S37AFKDC submitted 2025-05-09 cs.CR

Faneela , Baraq Ghaleb , Jawad Ahmad , William J. Buchanan , Sana Ullah Jan This is my paper · ORCID

classification cs.CR

keywords homomorphicencryptionCKKSsupportvectormachineencryptedinferenceprivacy-preservinglearninghybridkerneladaptivethresholdingcreditcardapproval

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

This paper tries to establish that credit-card approval can be performed on encrypted data without sacrificing the accuracy that a bank would get from a plaintext model. It presents PP-FinTech, a support vector machine (a standard classifier that learns a separating boundary) running entirely under CKKS, a homomorphic encryption scheme for approximate arithmetic on encrypted real numbers, using a hybrid polynomial–RBF (radial basis function) kernel for non-linear patterns and an adaptive threshold to absorb encryption noise. On the Credit Card Approval dataset, it reports 97.06% accuracy versus 97.51% for the plaintext hybrid model, with 44.9 ms per encrypted sample. A sympathetic reader would take away that fully encrypted inference for non-linear financial classifiers has moved into a practical range.

What carries the argument

The load-bearing object is the encrypted decision score $C(S')$, built by summing per-support-vector products $C(\alpha_j)\cdot C(K(X',SV_j))$ and adding the encrypted bias. The hybrid kernel $K=\lambda_1 K_p+\lambda_2 K_r$ is the mathematical mechanism that lets a CKKS circuit approximate non-linear separation, although the RBF component requires an unstated arithmetic approximation of $\exp(-\gamma\|X'-SV_j\|^2)$. The adaptive threshold $\theta=\lambda_1\mu+\lambda_2/\sigma$ is the post-processing mechanism tuned to keep classification stable as CKKS noise accumulates, and SIMD packing (processing many values in parallel within one ciphertext) is the performance mechanism that amortizes per-sample cost across a batch.

What would settle it

Inspect the implementation or re-implement Algorithm 2 from the paper. If the encrypted RBF evaluation is actually replaced by a plaintext kernel value, or if no polynomial approximation of $\exp(-\gamma\|x-\mathrm{sv}\|^2)$ exists within a three-level CKKS modulus chain, then the reported 97.06% accuracy cannot be produced by fully encrypted inference. A minimal check is to substitute a pure polynomial kernel and see whether accuracy collapses.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that the SVM decision score $C(S')=\sum_j C(\alpha_j)C(K(X',SV_j))+C(b)$ can be evaluated end-to-end under CKKS, and the resulting encrypted classifier performs almost indistinguishably from its plaintext counterpart: accuracy 97.06% vs. 97.51%, precision 98.33% vs. 96.82%, recall 95.16% vs. 96.58%, and F1 96.74% vs. 96.70%. The hybrid kernel combines a polynomial term (native to CKKS arithmetic) with an RBF term, and the adaptive threshold $\theta=\lambda_1\mu+\lambda_2/\sigma$ is meant to compensate for noise-induced shifts in the encrypted scores. The paper maintains that with careful parameter selection and SIMD batching, the 44.9 ms per-sample latency makes this practical for privacy-sensitive financial applications.

Load-bearing premise

The claim stands on the unstated assumption that the RBF kernel's exponential can be evaluated inside CKKS using only additions and multiplications with enough precision; the paper never specifies this approximation, and if it is wrong the reported accuracy is not from a real encrypted pipeline.

Editorial extensions

If this is right

A bank could outsource credit-card approval to an untrusted cloud while keeping both the applicant's features and the model's support vectors encrypted throughout, with only the final label revealed to the client.
Non-linear SVMs, which usually require operations homomorphic encryption cannot natively perform, become usable in encrypted form, widening the set of financial models that FHE can serve.
The reported 44.9 ms per-sample latency, if it holds, works out to roughly 20 decisions per second, within the range of interactive credit decisions.
The adaptive thresholding mechanism offers a general template for other encrypted classifiers: use batch statistics to shift the decision boundary and compensate for CKKS noise instead of enlarging parameters.

Reading between the lines

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

My inference: the strongest replication test is the RBF approximation. If no concrete CKKS-friendly polynomial approximation of $\exp(-\gamma\|x-\mathrm{sv}\|^2)$ is supplied, the reported accuracy may come from plaintext kernel evaluation, and the encrypted pipeline would not be complete.
My inference: because the adaptive threshold uses the mean and spread of decision scores, either the client receives a batch of scores to compute $\theta$, or the server computes encrypted batch statistics; the paper does not specify which, and each choice changes what information the server sees.
My inference: a natural ablation is to run the same CKKS pipeline with the polynomial kernel only; if accuracy stays near 97%, the RBF term is not actually contributing under encryption, and the hybrid kernel's role in the result would need re-examination.

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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 on arXiv:2505.05920. The headline claim doesn't hold up: there is no specified way to evaluate the RBF kernel under CKKS, and the adaptive threshold requires decrypting the decision score on the server. So the reported 97.06% accuracy and 44.9 ms latency cannot be attributed to a fully encrypted pipeline.

What's actually here: the paper trains a soft-margin SVM with a hybrid polynomial-RBF kernel on the UCI credit card dataset, then sketches a CKKS-based inference flow. The hybrid kernel idea is straightforward — a weighted sum of two kernels. The SIMD batching is a known optimization. The adaptive threshold is a heuristic. None of this is new; the authors themselves cite prior encrypted SVM work.

Where it falls down. The RBF kernel is the core problem. CKKS supports only polynomial arithmetic, so exp(-γ||x-sv||²) needs a polynomial approximation (e.g., Chebyshev or minimax) with a stated degree and interval. Algorithm 2 just writes Enc(K_j^r) ← K_r(Enc(X'), Enc(SV_j)) as if it were a homomorphic operation. No approximation, no depth budget, no precision analysis. Without this, the accuracy and timing numbers are not reproducible and may not reflect a real implementation. The adaptive threshold is worse: Algorithm 2 decrypts the decision score D on the server, then computes θ = λ1·μ + λ2/σ from the plaintext scores and returns sign(D−θ). That breaks the claimed end-to-end encryption — the server sees the scores. The 'scalability' result is just the per-sample latency multiplied by batch size, not an actual measurement.

Credit where due: the paper is clearly written and the experimental setup is standard. But these are not minor gaps; they are the entire mechanism of the scheme. The missing RBF approximation alone makes the central claim unverifiable.

Recommendation: desk reject. It's not ready for referees because the load-bearing technique is unspecified. If the authors supply the missing polynomial approximation and move the adaptive threshold to the client side, it could be a modest application paper, but as submitted it doesn't deserve referee time.

Referee Report

4 major / 4 minor

Summary. The paper proposes PP-FinTech, a privacy-preserving credit card approval system that combines the CKKS homomorphic encryption scheme with a soft-margin SVM using a hybrid polynomial-RBF kernel. The authors train the SVM in plaintext, encrypt the model parameters and test features, evaluate the SVM decision score homomorphically, and apply an adaptive threshold to the decision score to produce the final classification label. Experiments on the UCI Credit Card Approval dataset report 97.06% accuracy for PP-FinTech versus 97.51% for the plaintext hybrid model, with an average per-sample inference time of 44.9 ms. The paper claims that this demonstrates comparable performance to plaintext models while preserving privacy.

Significance. Privacy-preserving machine-learning inference is an important and timely problem, particularly for financial applications, and an encrypted SVM with a non-linear kernel would be a useful contribution. The paper correctly identifies the need to manage CKKS noise, reports both accuracy and runtime, and uses the OpenFHE library, which are appropriate methodological choices. However, at present the core technical claims are not substantiated: the encrypted RBF kernel evaluation is not specified in a way that is realizable under CKKS, and the adaptive thresholding step explicitly operates on decrypted scores. These issues are load-bearing for the claimed end-to-end encrypted pipeline, so the significance of the work as written is limited.

major comments (4)

[Section 4.2, Algorithm 2] The line Enc(K_j^r) ← K_r(Enc(X'), Enc(SV_j)) assumes that the RBF kernel can be evaluated under CKKS, but CKKS only supports additions and multiplications. Computing exp(-γ||X' - SV_j||^2) requires a polynomial approximation (for example, Taylor, Chebyshev, or minimax) with a stated degree, approximation interval, coefficient table, and multiplicative-depth budget. None of these are provided. Consequently, the reported 97.06% accuracy and 44.9 ms per-sample latency cannot be attributed to a real CKKS pipeline, and the experimental results are not reproducible from the manuscript.
[Section 4.2, Algorithm 2 and 'Secure Decryption and Classification'] Algorithm 2 explicitly decrypts Enc(D) to D before computing the adaptive threshold θ = λ1·μ + λ2/σ, and the following subsection applies the threshold to plaintext scores. This breaks the end-to-end encryption claim: the server learns the decrypted decision scores, and the threshold depends on the entire batch of test samples, which also leaks aggregate information about other users' scores. Computing μ and σ homomorphically is not described, and operations such as division and reciprocal required for λ2/σ are not native CKKS operations.
[Section 4.1, Section 4.2, and Table 1] The hybrid-kernel weights (λ1 = 0.7, λ2 = 0.3) and the adaptive-threshold weights (λ1 = 0.5, λ2 = 0.1) are tuned empirically, and the threshold directly determines the final class label. The paper does not state whether the reported accuracy was obtained on a held-out test set that was not used for this tuning. As written, the evaluation is circular: the threshold weights are chosen to maximize performance on the same data used to report the accuracy, so the claimed 'comparable performance to plaintext' is not established.
[Section 5.4 and Figure 4] The scalability evaluation is explicitly 'simulated' by scaling the observed per-sample latency, not measured on actual batched encrypted inference. This does not provide empirical evidence for the claimed SIMD-based speedup, especially since the text admits that SIMD was not benchmarked separately. In addition, Figure 4's y-axis is labeled in milliseconds but reaches only about 5 ms for 100 samples, which is inconsistent with a per-sample latency of 44.9 ms (100 samples would take roughly 4.5 seconds).

minor comments (4)

[Section 4.1] The sentence 'the Credit Card Approval dataset, which is which underwent multiple preprocessing...' contains a typo: 'which is which' should be removed.
[Section 5.1] The ROC curve discussion mentions that AUC is a key summary metric, but no AUC values are reported for either PT-FinTech or PP-FinTech, so the claim that the encrypted model is nearly identical is not quantified.
[Section 4.2] The CKKS parameter description would benefit from a table listing the exact modulus chain, scaling factor, ring dimension, and the number of levels, as well as the approximation parameters for the RBF kernel once they are provided.
[Section 5.2] The runtime breakdown attributes 7 ms to adaptive thresholding, but since Algorithm 2 computes the threshold after decryption, it is unclear whether this 7 ms corresponds to a homomorphic computation or to plaintext post-processing.

Circularity Check

2 steps flagged · score 6.0 of 10

Encrypted kernel output is specified as the encryption of the plaintext kernel result, and the adaptive threshold is fitted to the evaluated score distribution, so the reported encrypted accuracy is partly definitional and partly threshold-fitted rather than an independent homomorphic prediction.

self definitional [Section 4.2, 'Data Encryption and HE Kernel Evaluation' (encrypted decision score and hybrid kernel output equations)]
"The hybrid kernel output for each support vector is computed as: C(K(X ′,SV j)) = Enc(K(X ′,SV j)). ... The encrypted decision score is computed as: C(S ′) = Σ_j C(α j)·C(K(X ′,SV j)) + C(b)"

This equation defines the encrypted hybrid kernel output as the encryption of the plaintext kernel output. Substituting it into the encrypted decision-score formula makes C(S′) equal to Enc(Σ_j α_j K(X′,SV_j) + b) by construction, i.e., the claimed homomorphic decision score is just the encryption of the plaintext SVM score. Algorithm 2 writes K_r(Enc(X′), Enc(SV_j)) as if the RBF term were evaluated on ciphertexts, but no CKKS polynomial approximation, depth budget, or coefficient table is given, so the only explicit definitional equation reduces encrypted inference to encrypting a plaintext result rather than to computing on encrypted inputs.
fitted input called prediction [Section 4.2, 'Adaptive Threshold for Secure Classification' and Algorithm 2]
"θ=λ1·µ+λ2/σ ... The weights λ1 = 0.5 and λ2 = 0.1 were determined empirically and fixed to maintain consistent classification behavior across encrypted batches. ... return sign(D−θ);"

The final label is determined by comparing each decrypted score D to θ, where θ is computed from µ and σ of the same batch of decision scores plus empirically fixed constants λ1 and λ2. Thus every reported accuracy on the evaluation batch is produced by a decision boundary that depends on the batch's own score distribution and on constants fitted to the data. The 'prediction' is therefore not an independent test of a fixed plaintext or encrypted decision rule; it is a post-hoc threshold calibration whose fitted parameters directly influence the reported 97.06% accuracy.

full rationale

The core SVM formulation, CKKS background, and the idea of encrypting the trained decision function are standard and do not, by themselves, create a circular derivation; there are also no load-bearing self-citations, so the paper is not circular through a citation chain. However, two steps compromise the central claim that PP-FinTech performs real encrypted inference with 'comparable performance to the plaintext models.' First, the only explicit equation for the encrypted hybrid kernel, C(K(X′,SV_j)) = Enc(K(X′,SV_j)), defines the ciphertext kernel as the encryption of the plaintext kernel output; if that is the operative definition, the encrypted decision score is by construction the encryption of the plaintext SVM score, not a homomorphic evaluation. Second, the adaptive threshold θ = λ1·µ + λ2/σ is computed from the statistics of the very decision scores being classified, and the weights λ1=0.5, λ2=0.1 are stated to be 'determined empirically'; the reported accuracy is therefore partly a property of a fitted threshold rather than an independent prediction. The missing RBF polynomial approximation is also a reproducibility gap that prevents the reader from verifying that the 23.5 ms kernel-evaluation stage actually computes exp(-γ||x−sv||²) under CKKS. Together these issues make the headline accuracy and privacy-preserving inference claim partially circular by definition and by fitted post-processing, warranting a score of 6 rather than a clean non-finding.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The scheme rests on the unproven feasibility of computing RBF kernels under CKKS and on an adaptive threshold that requires access to plaintext decision scores.

free parameters (2)

Hybrid kernel weights λ1, λ2 = λ1=0.7, λ2=0.3
Tuned via grid search on a validation set (Section 4.1).
Adaptive threshold weights λ1, λ2 = λ1=0.5, λ2=0.1
Determined empirically and fixed to maintain classification behavior (Section 4.2).

assumptions (4)

standard math The SVM decision function can be evaluated using only additions and multiplications over encrypted values.
This is the basis of the CKKS evaluation (Section 2.2).
ad hoc to paper The RBF kernel can be approximated by a polynomial under CKKS without exceeding the noise budget.
No approximation is described in Algorithm 2 or elsewhere.
ad hoc to paper The adaptive threshold θ = λ1*μ + λ2/σ can be computed from encrypted decision scores without leaking information.
The paper does not explain how μ and σ are obtained; if decryption is required, the privacy claim fails.
domain assumption The Credit Card Approval dataset and the 80:20 split are sufficient for a stable performance estimate.
No cross-validation or significance testing is reported (Section 5).

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Cite this review

Pith. "Pith review of Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications." pith.science (2026). https://pith.science/paper/S37AFKDC

@misc{pith2026250505920,
  author       = {Pith},
  title        = {Pith review of: Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S37AFKDC}},
  note         = {Machine review of arXiv:2505.05920}
}

read the original abstract

The growing use of machine learning in cloud environments raises critical concerns about data security and privacy, especially in finance. Fully Homomorphic Encryption (FHE) offers a solution by enabling computations on encrypted data, but its high computational cost limits practicality. In this paper, we propose PP-FinTech, a privacy-preserving scheme for financial applications that employs a CKKS-based encrypted soft-margin SVM, enhanced with a hybrid kernel for modeling non-linear patterns and an adaptive thresholding mechanism for robust encrypted classification. Experiments on the Credit Card Approval dataset demonstrate comparable performance to the plaintext models, highlighting PP-FinTech's ability to balance privacy, and efficiency in secure financial ML systems.

Figures

Figures reproduced from arXiv: 2505.05920 by the authors.

**Figure 2.** ROC Curve Comparison 5.2 Computational Overhead We also evaluated computation time across all four models to measure efficiency. As shown in [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Empirical analysis of runtime and noise behavior during encrypted infer [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Simulated scalability: Total inference time across different batch sizes for [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

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