SearcharxivSearch

arXiv · 2608.26777

A Hybrid Post-Quantum Encryption Architecture with Self-Hosted Key Management for SME Cloud Data Protection

Abstract

Harvesting ciphertext from cloud storage needs no quantum computer; decrypting it later does. That gap is the harvest-now-decrypt-later exposure: anything protected by RSA or ECDH today that must stay secret for decades is already compromised. Small and medium-sized enterprises are least able to respond: they neither run the infrastructure on which their data sits on nor employ a cryptographer. Bespoke migration suits firms with security budgets; a managed key service relocates trust rather than removing it. The obstacle is architectural, not cryptographic. We present Quantum Cloud Guard (QCG), a software-only three-layer architecture. No prior SME-oriented system combines its three elements: client-side hybrid post-quantum encryption, self-hosted key custody with client-verifiable ML-DSA-87 signatures on served keys, and an integrated application-layer abuse-prevention gateway. Files never leave the client: each is sealed under AES-256-GCM, its key wrapped to an ML-KEM-1024 public key from the enterprise's key service. The enterprise alone administers it; it signs every key with ML-DSA-87, so a client that pinned it detects substitution. Separating key custody from data custody is the point: a provider holding both can read the data. On a 24 MHz STM32F407, ML-KEM-1024 key generation takes 40.8 ms and decapsulation 44.0 ms; on the server every post-quantum operation stays sub-millisecond, signing adding 0.24 ms per request. The service runs on a 4.49 EUR/month virtual server. Under sustained flooding, the in-process gateway Sentinel Gate rejected 98.8% of attack traffic while a legitimate client's median latency moved from 621 to 625 ms. Being single-source, this shows filtering effectiveness, not DDoS resilience.

Explore related subjects

Keep this discovery

BibTeXRIS

Muhammad Shaheer Bin Junaid. 2026-08-27. A Hybrid Post-Quantum Encryption Architecture with Self-Hosted Key Management for SME Cloud Data Protection. https://arxiv.org/abs/2608.26777

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural language processing (NLP), where training data can contain sensitive user text. This paper presents a controlled benchmark of membership inference vulnerability for text classification on the GLUE SST-2 sentiment dataset. A TF-IDF + Logistic Regression pipeline and a fine-tuned DistilBERT classifier are compared under a loss-threshold MIA, with utility measured by development accuracy and macro F1. DistilBERT reached 0.9466 accuracy and 0.9460 macro F1 against 0.8756 and 0.8727 for Logistic Regression, yet both models leaked membership signal (Attack AUC 0.5615 and 0.5800, respectively). Two mitigations were tested. Stronger regularization reduced leakage for Logistic Regression at a visible utility cost, whereas fine-tuning DistilBERT for 2 epochs instead of 3 reduced leakage with negligible accuracy loss. Lightweight training adjustments can improve the privacy-utility trade-off without complex defenses.

cs.CR

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

cs.CR

DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks

Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.

cs.CR