J. Cybersecur. Priv., Volume 6, Issue 1 (February 2026) – 7 articles

Explainability is increasingly expected to support not only interpretation, but also accountability, human oversight, and auditability in high-risk Artificial Intelligence (AI) systems. However, in many deployments, explanations are generated as isolated technical reports, remaining weakly connected to decision provenance, governance actions, audit logs, and regulatory documentation. This short communication introduces XAI-Compliance-by-Design, a modular engineering framework for explainable artificial intelligence (XAI) systems that routes explainability outputs and related technical traces into structured, audit-ready evidence throughout the AI lifecycle, designed to align with key obligations under the European Union Artificial Intelligence Act (EU AI Act) and the General Data Protection Regulation (GDPR). The framework specifies (i) a modular architecture that separates technical evidence generation from governance consumption through explicit interface points for emitting, storing, and querying evidence, and (ii) a Technical–Regulatory Correspondence Matrix—a mapping table linking regulatory anchors to concrete evidence artefacts and governance triggers. As this communication does not report measured results, it also introduces an Evidence-by-Design evaluation protocol defining measurable indicators, baseline configurations, and required artefacts to enable reproducible empirical validation in future work. Overall, the contribution is a practical blueprint that clarifies what evidence must be produced, where it is generated in the pipeline, and how it supports continuous compliance and auditability efforts without relying on post hoc explanations. Full article

Acoustic Side-Channel Attacks (ASCAs) exploit the sound produced by keyboards and other devices to infer sensitive information without breaching software or network defenses. Recent advances in deep learning, large language models, and signal processing have greatly expanded the feasibility and accuracy of these attacks. To clarify the evolving threat landscape, this survey systematically reviews ASCA research published between January 2020 and February 2025. We categorize modern ASCA methods into three levels of text reconstruction—individual keystrokes, short text (words/phrases), and long-text regeneration— and analyze the signal processing, machine learning, and language-model decoding techniques that enable them. We also evaluate how environmental factors such as microphone placement, ambient noise, and keyboard design influence attack performance, and we examine the challenges of generalizing laboratory-trained models to real-world settings. This survey makes three primary contributions: (1) it provides the first structured taxonomy of ASCAs based on text generation granularity and decoding methodology; (2) it synthesizes cross-study evidence on environmental and hardware factors that fundamentally shape ASCA performance; and (3) it consolidates emerging countermeasures, including Generative Adversarial Network-based noise masking, cryptographic defenses, and environmental mitigation, while identifying open research gaps and future threats posed by voice-enabled IoT and prospective quantum side-channels. Together, these insights underscore the need for interdisciplinary, multi-layered defenses against rapidly advancing ASCA techniques. Full article

The Cyber Kill Chain (CKC) is a prevalent concept in cyber defense; nevertheless, its emphasis on post-reconnaissance phases limits the capacity to foresee attacker activities outside the organizational boundary. This study introduces and empirically substantiates a pre-chain phase, referred to as contextual anticipation, which broadens the temporal framework of the CKC by methodically identifying subtle yet actionable signals prior to reconnaissance. The methodology combines the STEMPLES+ framework for socio-technical scanning with General Morphological Analysis (GMA), generating internally coherent scenarios that are translated into Indicators of Threats (IOT). These indicators connect contextual triggers to threshold-based monitoring activities and established courses of action, forming a reproducible and auditable relationship between foresight analysis and operational defense. The application of three illustrative cases—a banking merger, the distribution of a phishing kit in underground marketplaces, and wartime contribution scams—illustrated that contextual anticipation consistently provided quantifiable lead-time benefits varying from several days to six weeks. This proactive stance enabled measures such as registrar takedowns, targeted awareness campaigns, and anticipatory monitoring before distribution and exploitation. By formalizing CKC-0 as an integrated socio-technical phase, the research enhances cybersecurity practice by demonstrating how diffuse contextual drivers can be converted into organized, actionable mechanisms for proactive resilience. Full article

Statistical confidentiality focuses on protecting data to preserve its analytical value while preventing identity exposure, ensuring privacy and security in any system handling sensitive information. Homomorphic encryption allows computations on encrypted data without revealing it to anyone other than an owner or an authorized collector. When combined with other techniques, homomorphic encryption offers an ideal solution for ensuring statistical confidentiality. TFHE (Fast Fully Homomorphic Encryption over the Torus) is a fully homomorphic encryption scheme that supports efficient homomorphic operations on Booleans and integers. Building on TFHE, Zama’s Concrete project offers an open-source compiler that translates high-level Python code (version 3.9 or higher) into secure homomorphic computations. This study examines the feasibility of the Concrete compiler to perform core statistical analyses on encrypted data. We implement traditional algorithms for core statistical measures including the mean, variance, and five-point summary on encrypted datasets. Additionally, we develop a bitonic sort implementation to support the five-point summary. All implementations are executed within the Concrete framework, leveraging its built-in optimizations. Their performance is systematically evaluated by measuring circuit complexity, programmable bootstrapping count (PBS), compilation time, and execution time. We compare these results to findings from previous studies wherever possible. The results show that the complexity of sorting and statistical computations on encrypted data with the Concrete implementation of TFHE increases rapidly, and the size and range of data that can be accommodated is small for most applications. Nevertheless, this work reinforces the theoretical promise of Fully Homomorphic Encryption (FHE) for statistical analysis and highlights a clear path forward: the development of optimized, FHE-compatible algorithms. Full article

Online hate speech poses a growing socio-technological threat that undermines democratic resilience and obstructs progress toward Sustainable Development Goal 16 (SDG 16). This study examines the regulatory and behavioral dimensions of this phenomenon through a combined legal analysis of platform governance and an empirical survey conducted on Meta platforms, based on a sample of young Hungarians (N = 301, aged 14–34). This study focuses on Hungary as a relevant case study of a Central and Eastern European (CEE) state. Countries in this region, due to their shared historical development, face similar societal challenges that are also reflected in the online sphere. The combination of high social media penetration, a highly polarized political discourse, and the tensions between platform governance and EU law (the DSA) makes the Hungarian context particularly suitable for examining digital resilience and the legal awareness of young users. The results reveal a significant “awareness gap”: While a majority of young users can intuitively identify overt hate speech, their formal understanding of platform rules is minimal. Furthermore, their sanctioning preferences often diverge from Meta’s actual policies, indicating a lack of clarity and predictability in platform governance. This gap signals a structural weakness that erodes user trust. The legal analysis highlights the limited enforceability and opacity of content moderation mechanisms, even under the Digital Services Act (DSA) framework. The empirical findings show that current self-regulation models fail to empower users with the necessary knowledge. The contribution of this study is to empirically identify and critically reframe this ‘awareness gap’. Moving beyond a simple knowledge deficit, we argue that the gap is a symptom of a deeper legitimacy crisis in platform governance. It reflects a rational user response—manifesting as digital resignation—to opaque, commercially driven, and unaccountable moderation systems. By integrating legal and behavioral insights with critical platform studies, this paper argues that achieving SDG 16 requires a dual strategy: (1) fundamentally increasing transparency and accountability in content governance to rebuild user trust, and (2) enhancing user-centered digital and legal literacy through a shared responsibility model. Such a strategy must involve both public and private actors in a coordinated, rights-based approach. Ultimately, this study calls for policy frameworks that strengthen democratic resilience not only through better regulation, but by empowering citizens to become active participants—rather than passive subjects—in the governance of online spaces. Full article

The increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers. Full article

This study proposes the Huffman Tree and Binary Conversion (HTB) which is a preprocessing algorithm to transform the Huffman tree into binary representation before the encryption process. In fact, HTB can improve the structural readiness of plaintext by combining the Huffman code with a deterministic binary representation of the Huffman tree. In addition, binary representation of the Huffman tree and the compressed information will be encrypted by standard cryptographic algorithms. Six datasets, divided into two groups (short and long texts), were chosen to evaluate compression behavior and the processing cost. Moreover, AES and RSA are chosen to combine with the proposed method to analyze the encryption and decryption cycles. The experimental results show that HTB introduces a small linear-time overhead. That means, it is slightly slower than applying only the Huffman code. Across these datasets, HTB maintained a consistently low processing cost. The processing time is below one millisecond in both encoding and decoding processes. However, for long texts, the structural conversion cost becomes amortized across larger encoded messages, and the reduction in plaintext size leads to fewer encryption blocks for both AES and RSA. The reduced plaintext size lowers the number of AES encryption blocks by approximately 30–45% and decreases the number of encryption and decryption rounds in RSA. The encrypted binary representation of the Huffman tree also decreased structural ambiguity and reduced the potential exposure of frequency-related metadata. Although HTB does not replace cryptographic security, it enhances the structural consistency of compression. Therefore, the proposed method demonstrates scalability, predictable overhead, and improved suitability for cryptographic workflows. Full article