As systems migrate toward post-quantum readiness, symmetric keys are kept longer and protect larger data volumes, a regime in which keystream security beyond the birthday bound matters. The dominant block–cipher counter mode, AES-CTR (Advanced Encryption Standard, counter mode), caps keystream security at the birthday bound regardless of key length, and, to our knowledge, no lightweight stream cipher pairs provable beyond-birthday-bound security with a permutation standardised by the National Institute of Standards and Technology (NIST). We propose SoP-CTR, a stream cipher built from the NIST-standardised Ascon-p12 permutation via the Sum of Permutations (SoP) paradigm. The z-split SoP framework achieves single-key beyond-birthday-bound (BBB) security by XORing a split constant into the counter word, eliminating independent key derivation. We prove an explicit two-term pseudorandom-function (PRF) advantage bound Adv≤q3/2/2320+2pq/2256, valid for pairwise-distinct counters in [0,263). The online term is negligible throughout this 263-block range, and the offline term bounds adversarial key-search and is the binding constraint for post-quantum offline budgets. The SoP construction provably eliminates the birthday distinguisher that breaks a hypothetical single-call CTR-Ascon at q≈2160. We also give the first quantum analysis of this domain-separated single-key variant in the Q1 model, where construction queries are classical and the public permutation may be evaluated quantumly, obtaining an attack at 285.3 and an unconditional lower bound of 264.3, so the post-quantum figure is the best known attack cost of 285.3 rather than the generic 2128 Grover level. On a physical STM32F407 ARM Cortex-M4 (168 MHz), hardware cycle-counter measurements yield 61.44 cycles per byte (cpb) for the unrolled variant, about 4.6% faster than Ascon-128 keystream extraction (64.39 cpb), and 63.78 cpb for the memory-optimised rolled variant (3832 bytes Flash), while providing a strictly stronger BBB guarantee. An x86-64 AVX2 two-block variant achieves 2.57 cpb at 1 KiB.
Blockchain-based electronic voting has yet to reach the electoral environments that could benefit from it most. Existing systems are designed for well-connected, grid-powered urban settings and fail precisely where digital voting could widen participation the most; this gap motivates a framework engineered from the ground up for low-infrastructure conditions rather than one adapted to them. Across much of Africa, four deployment barriers stand in the way: intermittent connectivity that excludes an estimated 43% of the population, identity documents that remain valid for up to ten years and degrade conventional face recognition from 99% to below 80%, power demands of 200–250 W that rule out battery-powered operation, and cryptographic designs locked to a single algorithm regardless of operating context. This paper presents AdaptVote, a five-layer framework built on the premise that these barriers must be removed jointly rather than in isolation. At its core, the AI-FOLM algorithm anchors INT8-quantised ArcFace embeddings to age-stable craniofacial ratios on a Xilinx Zynq-7020 FPGA, reaching a 96.7% True Accept Rate at 0.1% False Accept Rate over 6–10 year age gaps—2.2 percentage points beyond the state of the art. A nullifier-based protocol allows ballots to be cast entirely offline with Merkle-tree integrity, an adaptive ECDSA/EdDSA/BLS layer cuts Ethereum settlement costs by 60–86%, and the complete polling station runs at 4.2 W peak power and 28 Wh per 12 h session, a 99.3% energy reduction over GPU baselines. A twelve-hour field deployment with 300 voters at Mohammed First University, Nador, Morocco, conducted under 35% network availability, recorded a 95.7% first-attempt authentication rate (95% CI: 92.7–97.5%), 99.2% system uptime, and a 53.3% reduction in average voting time relative to paper ballots (p<0.001). Together, these results suggest that trustworthy electronic voting can be engineered for, rather than merely adapted to, low-infrastructure electoral settings.
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning and deep learning algorithms, as well as Large Language Models (LLMs). Various feature types—static, dynamic, hybrid, textual, and image-based—were considered. The results showed that concept drift is widespread and significantly affects model performance. Factors influencing the drift include feature types, data environments, and detection methods. Balancing algorithms help with class imbalance but do not fully address drift, which primarily stems from the dynamic nature of malware. No strong link was found between the type of algorithm used and concept drift; the impact was relatively minor compared to other variables because hyperparameters were not fine-tuned, and the default algorithm configurations were used. The LLM evaluation is treated as an exploratory baseline because the original feature spaces were compressed using PCA to satisfy token-length constraints. Under this setting, LLMs showed promising few-shot performance but remained sensitive to temporal drift.
Graph neural networks (GNNs) have become a dominant paradigm for learning over graph-structured data. To protect data privacy in distributed graph settings, federated GNNs have emerged as a promising solution by enabling collaborative model training without raw data sharing. However, recent studies demonstrate that federated GNNs can inherit and even amplify biases from distributed data, resulting in unfair global models. While state-of-the-art (SOTA) approaches have introduced fairness-aware federated GNN frameworks, they overlook the privacy risks arising from client–server communications during training. To address this gap, we propose SaFeGNN, a Secure and Fair Federated Graph Neural Network framework that jointly enforces privacy protection and fairness guarantees. SaFeGNN secures the communication process via additive secret sharing and client-level differential privacy, achieving stronger security guarantees compared to existing solutions. Experimental results show that SaFeGNN maintains performance close to that of the baseline, with only an additional overhead of 2.7 s and 3.6 MB per global round.
Security Operations Centers increasingly use artificial intelligence to rank alerts, summarize evidence, and automate repetitive response actions. However, AI-enabled security operations can also create new risks for incident response and digital forensic reliability, including false-negative prioritization, model drift, hallucinated explanations, prompt injection, automation bias, unsafe SOAR actions, and evidence contamination. This article proposes a risk-driven deployment and forensic readiness framework for AI-enabled Security Operations Centers. The framework combines Monte Carlo loss simulation, detector threshold analysis, analyst queueing, model-drift monitoring, and evidence-preserving governance controls. It explicitly separates evidence, recommendation, and action so that AI can accelerate triage while preserving source artifacts, provenance, audit trails, and chain-of-custody information needed for incident reconstruction. Illustrative simulation results show a mean annualized loss expectancy of USD 1.70 M, a 95% Value-at-Risk of USD 3.85 M and a 99% Value-at-Risk of USD 6.29 M, a detector ROC-AUC of 0.942 and PR-AUC of 0.750 with precision 0.719, recall 0.650 and F1 0.683 at the selected decision threshold of 1.873, a manual triage workload reduction of about 38%, and a reduction in mean time to respond (MTTR, defined throughout as mean time to respond rather than mean time to resolution) from about 44 to 26 min under controlled assumptions. The results are demonstration outputs rather than universal benchmarks. The main contribution is a reproducible governance method for deciding when AI reduces SOC risk, when it transfers risk, and when forensic readiness requires human approval, evidence preservation, or automation rollback.
Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness. Neither reveals whether working code was broken. We evaluate eight commercial and open LLMs on 922 JavaScript vulnerability patches, scoring neutralisation and functional preservation. Lacking tests, we score at scale with a reference-based LLM judge, calibrated against execution on a 144-patch benchmark and 254 Java-CVE patches, plus a cross-family judge. The best model fixes 23% of vulnerabilities (judge-based), and cost-efficiency inverts the accuracy ranking. Our central finding concerns the instrument: both judges flag more over-fixes than execution confirms (precision 5–10%), yet on the functional axis agree far more with each other (κ=0.75) than with execution (κ≤0.26), so judge–judge agreement measures reliability, not validity. On real Java code the over-reporting persists, while the judges’ correctness estimates diverge, leaving no single judge trustworthy. Over-fixing is genuine but, under execution, uncommon: a few percent of vulnerability-removing patches, or under 2%, excluding one artefact-prone scenario, both lower bounds. Only adequately tested execution measures the functional-preservation rate, so security-patch evaluation must run the code, use a judge only to rank models, and weigh costs. We release the harness and executable benchmark.
The increasing dependence of financial supply chains on digital infrastructures has made it more necessary to design secure, resilient, and reliable networks than ever before. This research presents a self-healing framework based on blockchain and digital twins for multi-objective fuzzy optimization of financial supply chains under uncertainty. The proposed model, focusing on minimizing financial cost, cyber risk, and recovery time while simultaneously maximizing the level of trust and resilience, enables intelligent decision-making in the face of cyber threats. By combining real-time monitoring, secure transaction validation, fuzzy risk assessment, and automated recovery, the framework identifies the role of each component in maintaining the financial and operational stability of the network. The results showed that the complete model achieved an overall performance score of 0.944 in the component elimination study and increased the level of trust and resilience to 0.95 and 0.96, respectively. The cyber risk index was also maintained at 0.118, indicating the framework’s ability to control threats and maintain network stability. The findings show that the convergence of blockchain, digital twin, fuzzy logic, and self-healing mechanism can provide an effective basis for the development of smart, secure, and resilient financial supply chains.
Directive (EU) 2022/2555 (NIS2) designated healthcare a sector of high criticality, with a transposition deadline of 17 October 2024. Only four of twenty-seven Member States met it: Croatia transposed eight months early and Italy one day before the deadline. Because the formal regulatory gap between them is small—and, on the primary instrument, favours Croatia—this paper asks not whether but how two Member States with near-identical transposition timelines diverge in the operational practice and transparency of healthcare-sector incident reporting. Drawing on neo-institutional theory and the economics of information security, it synthesises the literature and compares the two transposition instruments against the primary legal texts. Both reproduce the NIS2 notification timeline faithfully, so divergence cannot be attributed to differing statutory obligations. Public-reporting transparency is therefore operationalised as a measurable dependent variable: Italy’s Agenzia per la Cybersicurezza Nazionale (ACN), an autonomous agency since 2021, publishes healthcare-specific data, whereas Croatia’s National Cybersecurity Centre (NCSC-HR)—competent authority only since 2025—publishes only aggregate figures. The asymmetry is autonomy and mandate, not institutional age. A portable four-indicator transparency index is proposed and demonstrated in a two-coder pilot (κ_w = 0.80); an independent incident-composition cross-check is consistent with the asymmetry.
Risk registers remain static governance artefacts, manually maintained and weakly coupled to operational evidence. While organisations generate continuous security telemetry from vulnerability scanners, incident reports, and audit findings, this evidence is rarely synthesised into coherent, evolving risk structures. Existing approaches address fragments of the problem: SIEM systems correlate events but do not construct risk registers; GRC platforms manage risk documentation but depend on manual entry; and LLM applications assist with summarisation but introduce non-determinism incompatible with governance requirements. This paper presents a hybrid architecture that separates stochastic LLM-based extraction from deterministic risk correlation and aggregation. The system ingests heterogeneous evidence, extracts structured claims via schema-bounded LLM processing, and correlates events into stable risk trees using anchor-based tiered matching. All correlation and projection operations are deterministic and replayable. The contribution is an architectural design pattern for integrating probabilistic inference into governance systems without compromising auditability. The walkthroughs run on a reference prototype. Replaying the stored evidence three times rebuilt the same register state, and admission scores matched the values the rules predict. An injected malformed extraction was quarantined; the register did not change.
This paper presents a secure data sharing platform that organises KR-IBI, KR-IBE, KR-PEKS, and KR-PAEKS into an end-to-end Rust/Tauri workflow for registration, authentication, encrypted upload, searchable retrieval, and authorised decryption. The work addresses a deployment-level composition problem rather than proposing a new primitive: practical data sharing requires coordinated credential handling, payload representation, searchable indexing, session control, and record integrity across schemes with distinct interfaces. The platform supports text, file, and image payloads through a hybrid KR-IBE/HKDF-SHA-256/AES-256-GCM layer. Fresh KR-IBE key material is generated by uniformly sampling a nonzero scalar and multiplying the Ed25519 prime-order subgroup generator, providing approximately 252 bits of min-entropy before HKDF derivation. An evaluation with 100 repetitions per configuration over Enron-derived workloads containing 100–10,000 records and 1, 5, 10, or 20 authorised identities achieved 100/100 correctness for authorised retrieval and decryption, wrong-keyword and wrong-scheme rejection, and unauthorised-access rejection. KR-PEKS search latency ranged from 29.26 ms at 100 records to 3023.82 ms at 10,000 records, whereas KR-PAEKS ranged from 775.53 ms to 93,045.52 ms. These results quantify the performance distinction between the lower-latency KR-PEKS mode and the sender-authenticated searchable encryption provided by KR-PAEKS.
The widespread use of Quick Response (QR) codes increases exposure to QR-code-based phishing, or quishing. This study examines the mechanisms, user behaviors, and contextual conditions that shape QR-mediated risk from a sociotechnical perspective. A structured literature review and narrative synthesis were conducted using four documented search strings. Following deduplication, screening, retrieval, and full-text assessment, 27 studies were included from 71 identified records. Two reviewers independently evaluated methodological quality using six criteria. The corpus comprised 16 technical-detection studies, five user-centered or behavioral studies, two attack demonstrations or simulations, and four reviews or preventive frameworks. The mean consensus quality score was 10.04 out of 12; 19 studies were classified as high quality and eight as moderate quality. The synthesis indicates that quishing exploits the interaction of contextual legitimacy, routine scanning, limited destination visibility, and insufficient verification before navigation or disclosure of sensitive information. These findings informed an attack lifecycle, a sociotechnical model, a user security decision flow, a risk–protection mapping, and multilevel recommendations. These literature-derived artifacts are conceptual and heuristic rather than empirically validated. Effective mitigation therefore requires QR-specific verification mechanisms combined with behavioral and technical safeguards for users, interfaces, organizations, and platforms, followed by expert, usability, and experimental validation.
(1) Background: The digital traces of unmanned aerial vehicles (UAVs) are becoming increasingly important in criminal incidents, the violation of airspace and in military operations, thus making the reconstruction of the digital traces a critical task. But, current tools like DatCon, Autopsy and GRYPHON cannot recover telemetry when the flight logs are logically damaged, fragmented or partially deleted and don’t offer any quantitative measurement of the confidence of the recovered information. (2) Methods: A unified hardware-software complex, including a forensic workstation, a hardware write-blocker and SD/microSD/eMMC adapters; a set of software modules for extracting artifacts from files, structural parsing of DAT/BIN/CSV log, neural network reconstruction of missing telemetry using a two-layer LSTM architecture; a multi-source correlation module that combines flight logs, telemetry, media metadata and controller artifacts; a module, Confidence Score (CS), that computes a reliability measure in [0,1]; and a visualization module to generate a reconstructed trajectory on an electronic map. (3) Results: The complex has been tested on 105 flights on 10 different UAVs, 492 flight logs were gathered, 10,435 were the media item files and 624 GB was the amount of storage during acquisition. The carving stage recovers 98.7% of artifacts across the eight signature classes, the LSTM module recovers all five telemetry parameters with R2>0.99 and a single-step horizontal position error of 6.8 m, which is reduced to 4.7 m after multi-source correlation (below the 5 m operational target consistent with consumer-GNSS precision); the dependence on gap length is described by the empirical growth law εhoriz≈4.84·G1.44 m; 46.8% of recovered records fall within the high-confidence band of CS≥0.8; and the complex outperforms DatCon, Autopsy + DJI Analyzer and GRYPHON by 22–35 percentage points in end-to-end record recovery and by a factor of ∼2.6 in mean horizontal error (4.7 m vs. 12.4–18.7 m). (4) Conclusions: The combined write-blocked hardware acquisition, neural reconstruction of telemetry, and quantitative confidence index provides a forensically structured pipeline that fills an existing gap in UAV digital forensics; we note that technical reconstruction accuracy does not by itself confer legal admissibility, which remains a function of jurisdiction-specific evidentiary standards discussed in the Conclusions.
Adversarial machine learning (AML), reinforcement learning (RL), and explainable artificial intelligence (XAI) are increasingly studied as separate problems, yet their interactions under realistic threat conditions remain poorly understood. This review addresses that gap through a systematic analysis of 207 studies selected from 4447 records following the PRISMA 2020 guidelines, covering work published between 2020 and 2026 across cybersecurity and computer vision. A taxonomy of adversarial attacks is constructed across training and inference phases, defense mechanisms are examined with attention to their documented failure modes, and robustness evaluation practices are assessed across the surveyed literature. RL is analyzed in both offensive and defensive roles. Attack agents using RL achieve evasion rates of 74–97% against ML-based detectors, while RL-based defenses report robustness gains of up to 3× over static baselines under comparable threat conditions. XAI receives particular attention because the field treats it almost exclusively as a transparency mechanism, whereas the reviewed evidence shows that it also functions as an attack surface. Attribution methods such as LIME, SHAP, and Grad-CAM produce unreliable explanations under adversarial perturbation, and no system in the reviewed literature certifies that attribution properties are maintained when inputs are manipulated. The review concludes with an analysis of open problems and research directions for building systems that are robust against adaptive adversaries, interpretable under operational constraints, and auditable in environments where AI accountability is a legal requirement.
Small and medium-sized enterprises (SMEs) have limited resources and governance that might restrict their ability to conduct dynamic cyber risk assessment (DCRA) and maintain effective cyber situational awareness (CSA). This study investigates stakeholders’ perceptions of CSA, DCRA, and AI-enabled cybersecurity to develop a conceptual framework targeted for SMEs. The online survey was cross-sectional, and 302 completed responses were gathered. The valid sample size for the items ranged from 288 to 299. Out of 293 respondents, 54 (18.4%) indicated prior usage of CSA techniques, 21 (7.2%) reported prior use of DCRA tools, and 226 (77.1%) backed AI in the cybersecurity field. The highest rated DCRA requirements were continuous threat updates, identification of attacks and vulnerabilities, and prioritization of alerts based on risk. The highest rated implementation challenges were accuracy, relevance, and integration with current infrastructure. Four multi-item measures had good-to-outstanding internal consistency (α = 0.868–0.926; ω = 0.870–0.929), and parallel analysis supported a single factor for each. Exploratory findings suggested that Information Technology (IT) and cybersecurity professionals had greater familiarity with CSA and DCRA than did leaders and managers. There was a moderate-to-strong positive association between familiarity with CSA and DCRA (ρ = 54). The framework defines AI as a layer of analytical decision support, DCRA as the process of translating changing evidence into updated and prioritized risk information, and CSA as decision-relevant interpretation and use of that information. This framework will help SMEs to improve CSA and will help their leaders to make the right decisions when dealing with cyber threats.
Tokens are widely used to secure client–server communications in systems based on automatic authentication. These tokens can be vulnerable to hacking, as an attacker can impersonate a real user by eavesdropping on their communications. In this paper, we propose a new authentication mechanism that generates a random token for each authentication. The token consists of confidential data and is encrypted using random coordinates from a securely stored confidential image. The client uses a random session key to encrypt the confidential image, then encrypts the token using randomly selected coordinates by matching ASCII character values with pixel values. The results and analysis demonstrate improved resistance to credential theft, replay attacks, and passive eavesdropping under the stated security assumptions. Even if hackers crack the encrypted token, decryption is difficult because the encryption method relies on values unrelated to the original authentication data. Comparison results also demonstrate efficiency and reliability compared to existing systems, as well as their ability to withstand brute-force attacks, with the entropy of the probability distribution being the best.
As organizations lean more heavily on their IT systems, managing cyber risk is gaining increasing importance. Organizations are often challenged to determine which cybersecurity risk framework they should adopt. Choosing the right framework can have a significant impact on the quality of governance, operational resilience, and assurance in risk reporting. However, most prevalent cybersecurity risk frameworks vary significantly in their intent, design, and analytical approach. This makes it difficult for organizations to understand how each framework may meet their business needs. This study presents an AI-enhanced multi-criteria decision support approach for evaluating cybersecurity risk frameworks. The model incorporates machine learning-driven risk scoring as a conceptual input layer, enhancing the objectivity and analytical rigor of the comparison without executing new predictive algorithms. The methodology includes a hybrid approach of literature review, document analysis, and multi-criteria decision analysis (MCDA) to compare and rank NIST CSF, ISO 27001, FAIR, OCTAVE, and CRAMM based on eight criteria that are designed to represent modern requirements for risk frameworks, including governance, scalability, quantitative focus, and interoperability. These criteria also reflect differences in security metrics supported by each framework to provide an organized means to compare qualitative versus quantitative measurement methodologies. The results indicate that NIST CSF performs the best overall in agility, business alignment, and interoperability. ISO 27001 outperforms all others in established governance and compliance. FAIR outperforms all others in quantitative risk analysis and provides superior analytical depth that other frameworks do not offer. OCTAVE and CRAMM function well in legacy systems but lack scalability and are not well-suited for modern distributed systems. Robustness analysis shows that the ranking of NIST CSF, ISO 27001, and FAIR is consistent under different weighting combinations and industry types. The result of this research demonstrates that a combined or hybrid approach to cybersecurity risk framework selection, such as using NIST CSF with FAIR, can give organizations a more well-rounded foundation for applying machine learning-enabled risk analytics with cyber controls. This research also offers a reusable decision support tool that organizations can leverage when aligning their risk priorities to the features of cybersecurity risk frameworks.
Cyberattacks have become a routine feature of contemporary security environments, yet policy responses often treat cyber threats as undifferentiated, encouraging generic remedies while obscuring the distinct capabilities needed to address different forms of attack. This article develops an empirical exploratory typology of cyber threats affecting Thailand. Drawing on incident-level data, it uses multiple correspondence analysis and hierarchical clustering to classify attacks by actor type, motive, target industry, event type, event subtype, and attributed actor country. The findings reveal three distinct threat profiles: financial cybercrime, characterised by criminal actors and financial motives; hacktivist disruption, defined by protest motives, disruptive operations, and attacks on public administration; and nation-state political espionage, associated with state-linked actors, China-attributed activity, and exploitation of end hosts. The article argues that distinguishing among these threat profiles provides a more useful basis for threat prioritisation, capability development, and resource allocation than treating cyber insecurity as a single risk category.
This paper presents TALOS, a unified reusable 6G CryptoProcessor architecture for high-assurance symmetric security services under a 256-bit private-key baseline. The design addresses a core hardware challenge in future mobile systems: supporting heterogeneous strong symmetric primitives without duplicating complete cipher cores. TALOS combines a Hierarchical Common Data Path (HCDP) with a three-tier cryptographic encapsulation model spanning AES-256, Snow 5G/SNOW-V-class, and ZUC-256. Tier-1 captures native nonlinear substitutions, Tier-2 compiles bounded arithmetic nonlinearities into exact micro-S-boxes, and Tier-3 consolidates shared permutation, XOR, affine, diffusion, and state-transport fabrics. This decomposition preserves cipher correctness while exposing realistic sharing opportunities across substitution, arithmetic, and linear transport layers. The architecture also supports confidentiality processing and integration with integrity- and authentication-oriented service logic through a common control/resource framework. Compared with monolithic universal-box or loosely aggregated multi-core approaches, TALOS provides a disciplined, RTL-oriented taxonomy for crypto-agile symmetric-core hardware. The proposed framework advances 6G cryptographic hardware design by combining operator-exact reuse, architectural scalability, and implementation-oriented efficiency within a single CryptoProcessor paradigm.
Homomorphic Encryption (HE) has emerged as a promising approach for data processing without exposing sensitive information. Despite significant advances, the practical strategies for the integration of HE into widely used database management systems (DBMSs) remain limited due to performance constraints and architectural challenges. This paper explores HE integration strategies within DBMS, focusing on SQL Server, PostgreSQL, and MariaDB. A methodology is proposed to assess the feasibility and performance of multiple HE schemes, including BFV, CKKS, BGV, TFHE, Paillier, and RSA (without padding). The evaluation considers different integration strategies, namely Python-based execution and native C++ extensions, across Windows and Debian environments. Experimental results obtained from four configurations demonstrate that the choice of HE scheme and integration strategy significantly impacts performance. Lattice-based schemes (BFV, CKKS, BGV) provide a balanced trade-off between functionality and efficiency, while TFHE incurs high computational costs due to its bit-level design. Native C++ integrations consistently outperform Python-based approaches, although the latter offer greater flexibility and ease of development. The findings highlight the feasibility of integrating HE into DBMS while emphasizing the importance of selecting appropriate schemes and integration mechanisms to meet application-specific requirements. The proposed evaluation framework provides preliminary insights into the relative behavior of different HE schemes and integration strategies under controlled experimental conditions, supporting future work on privacy-preserving DBMS design.
This paper proposes a model for account-holding proofs across multiple authorities and presents a concrete construction from JWT-derived evidence, enabling a verifier to evaluate the resulting artifact under specified system assumptions and acceptance policies. Conventional account linkage approaches often depend on particular identity providers (IdPs) or linkage mechanisms and may expose or correlate more credential information than is necessary for the verifier’s policy. To address this, we formalize a scheme-agnostic abstract model and organize its security, disclosure-related, and deployment requirements. As a concrete instantiation, we apply an RSA-based synchronized aggregate signature scheme to encoded messages derived from the RS256 preprocessing step of JWT signing inputs. The resulting artifact is not a standard JWT and is not intended for direct verification by existing JWT/OIDC verifiers; rather, it provides a single aggregate signature component over multiple JWT-derived evidence items. Through analytical and prototype-based evaluation, we show that the signature-component data to be presented is reduced from n individual components to a single aggregate component and that the exponentiation applied to the presented signature component is reduced from O(n) to O(1), while the overall verification remains dominated by per-message public-key terms. The results of prototype implementation indicate that aggregate verification is not faster than ordinary RS256 JWT verification under the evaluated parameters; therefore, the construction is better suited to one-time or low-frequency account-holding proof scenarios in which the additional latency is tolerable.
