Search Results (259)

Conventional cosmetic human studies rely on pre–post mean comparisons, which have limitations in explaining where and how facial skin changes occur. This pilot single-arm study proposed a privacy-preserving three-dimensional (3D) facial mesh mapping framework and demonstrated its application using an illustrative dataset obtained from participants who used a polydeoxyribonucleotide (PDRN)-containing cosmetic. Twenty-two participants underwent facial skin assessments before and after product use. Conventional analysis included pre–post comparisons of elasticity-related parameters. Additionally, 3D facial images obtained via stereophotogrammetry were converted into de-identified mesh surfaces, spatially aligned between time points, and visualized using color-coded heatmaps. For each participant, the left facial panel displayed changes in a skin hydration permittivity index, while the right panel displayed changes in the R2 gross elasticity parameter (Ua/Uf). Overall mean values tended to increase after product use; however, the 3D visualization revealed heterogeneous spatial patterns undetectable via mean values. This method improved spatial matching, enabled intuitive regional comparison, and reduced privacy concerns by removing identifiable facial features. The privacy-preserving 3D facial mesh mapping (P3DMM) framework may serve as a complementary tool for cosmetic human studies, enabling the generation of structured, de-identified spatial datasets for future skin response research. Full article

The article develops a hybrid multimodal neural network for the automatic prevention of personal digital rights violations, focusing on improving security through anomaly detection and ensuring data confidentiality. The main aim is to integrate several innovative methods, such as federated learning, gating, latent competitive learning, and a variational autoencoder, to improve violation detection accuracy. The key contribution is the development of a training mixture that combines a probabilistic anomaly detector and an autoencoder reconstruction signal, which allows for effective detection of typical incidents and hidden anomalies. The experimental evaluation results showed high-performance indicators, with ROC-AUC at 0.96 and accuracy at 0.94, confirming the system’s effectiveness on anonymized data. The results obtained have a significant practical contribution, as they can be integrated into national information security systems, including SOC and forensic reports, which will ensure a higher level of personal data protection and reduce privacy breach risks. The scope of the proposed system simultaneously covers cybersecurity, personal data protection, national security, SOC systems, and forensic analysis. Full article

In recent years, electronic commerce (e-commerce) platforms catering to Saudi users have experienced significant growth. Analyzing the privacy policies of these platforms is crucial to ensure data protection and transparency for Saudi users, especially in light of Saudi Arabia’s Vision 2030. However, existing studies on these platforms are limited in scope and fail to address key dimensions comprehensively. This study investigates the current state of privacy policies across 500 e-commerce websites serving Saudi users. The analysis focuses on policy availability, language, readability, and compliance with Saudi Arabia’s Personal Data Protection Law (PDPL). The findings reveal that 19.40% of websites lack privacy policies, and 2.01% fail to provide an Arabic version. On average, the privacy policies are lengthy, with approximately 981 words and 115 sentences, and are written in complex language that is difficult for users to understand. This study also identifies gaps in compliance with the PDPL, particularly in clarifying how data is collected and processed, and in explaining user rights. This study highlights the need for clearer, more accessible, and legally compliant privacy policies to enhance user trust and data protection. Full article

Face recognition technologies are increasingly deployed in transport infrastructures to improve efficiency and security, but they raise significant privacy and data protection concerns. This study reviews how privacy-preserving face recognition techniques can address these challenges in real-world settings. Using a systematic literature review approach, the paper analyses research across technical, operational, and governance perspectives. The findings show that while advanced methods such as encryption, federated learning, and de-identification can reduce data exposure, they are rarely implemented in operational systems, which tend to prioritize performance and scalability. At the same time, governance-focused studies emphasize issues such as proportionality, accountability, and fundamental rights, often without clear links to technical solutions. Overall, the review highlights a fragmented landscape and a gap between research and practice, underscoring the need for integrated approaches that align privacy-preserving techniques with practical deployment constraints and regulatory requirements. Full article

Public deployment of generative AI in cultural institutions is constrained by small, rights-restricted datasets, strict latency and runtime-stability requirements, and limits on visitor-data collection. This study presents a deployment-oriented framework for adapting a pre-trained text-to-image diffusion foundation model to a heritage-specific visual domain using Low-Rank Adaptation (LoRA). A Stable Diffusion v1.5 backbone is specialized through data-centric curation and LoRA fine-tuning, then served through an asynchronous edge architecture that links a Unity client and a local Python (version 3.10) inference server for public-facing operation on a native 400 × 1080 vertical canvas. To support deployment decisions without collecting personally identifiable information, the system records only anonymous operational logs and evaluates sustained-load behavior under repeated inference. In a 1000-iteration profiling test, the proposed configuration maintained stable runtime behavior without observable upward memory drift, with a peak allocated VRAM of 3.04 GB and an average end-to-end latency of 3.12 s. An 8 h field deployment further indicated service continuity under public interaction, while a CLIP-based proxy analysis under matched prompts and seeds suggested improved relative style controllability after adaptation (0.848 vs. 0.799). Rather than claiming cultural authenticity or visitor-level effects, this study offers a data-centric, deployment-oriented methodology for operating public-facing generative AI under small-data, latency, and privacy constraints. Full article

With the growing volume of sensitive data stored and processed in cloud environments, conventional security models are no longer sufficient to guarantee privacy, integrity, and trust. This paper proposes a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) and homomorphic encryption (HE) to enable secure and privacy-preserving data sharing. ZKPs are employed to verify user access rights without exposing identities or underlying information, while HE allows computations to be performed directly on encrypted data, ensuring confidentiality is preserved throughout the data lifecycle. The proposed framework addresses the limitations of existing approaches that either lack encrypted computation capabilities or expose sensitive data during processing. Formal and informal analyses demonstrate the feasibility of the model in terms of encryption time, ZKP verification latency, and computation overhead. The framework is designed to be applied initially in the healthcare sector and aligns with national digital transformation initiatives such as Saudi Vision 2030. Full article

Mobile apps are essential for communication, transactions and leisure and frequently rely on access to personal data. This study examines Google Play’s Data Safety section and declared permissions five years after the GDPR came into force, focusing on how developers disclose data collection, sharing, security practices and deletion controls. We use metadata from 49,578 Android apps and analyze self-reported disclosures in relation to permission categories, app categories, installs and user ratings. The results show that free apps request broader permission access than paid ones and that declared permission use has gradually increased over time. In addition, 25.44% of the sampled apps had not completed any part of the Data Safety section and non-completion was associated with app age, installation band and pricing model. Among apps with completed relevant Data Safety section disclosures, 11% of developers explicitly declared that data are not encrypted in transit and 34% explicitly declared that no user-initiated data deletion mechanism is available. Category-level differences in declared data collection and sharing were modest, while the relationship between permission breadth and user ratings was small. Overall, the findings indicate that structured disclosure mechanisms can improve visibility of privacy-related information, but do not necessarily ensure its completeness or consistency. Full article

We propose stateful order-preserving encryption (SOPE), a novel framework designed to realize human-centric data security and privacy, the fundamental values of the Fifth Industrial Revolution. Conventional order-preserving encryption supports efficient queries in cloud databases but fundamentally leaks plaintext distributions, leaving data vulnerable to inference attacks. To mitigate this vulnerability while maintaining query efficiency, SOPE introduces a partition-based dynamic density adjustment mechanism under an honest-but-curious threat model. This mechanism offsets density imbalances between partitions in real time by inserting decoy ciphertexts, thereby limiting the leakage scope to the order of data while obscuring frequency information. Our analysis and empirical evaluations demonstrate that SOPE’s ciphertexts consistently approach a uniform distribution by adaptively compensating for the underlying plaintext distribution through decoy insertion. While the continuous insertion of decoy ciphertexts inevitably incurs additional storage overhead (controlled by a tunable parameter $\lambda$), our evaluations demonstrate practical performance. By striking an optimal balance between efficiency and human privacy rights, SOPE provides a trustworthy infrastructure for secure data utilization. Full article

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. As these models are increasingly deployed in real-world and regulated settings, the consequences of such memorization become practical and high-stakes, reinforced by data-protection frameworks that grant individuals a Right to be Forgotten (e.g., the GDPR). Simply removing a record from the training dataset does not guarantee the elimination of its influence from the model, while retrain-from-scratch procedures are often prohibitive for modern architectures, including Transformers and Large Language Models (LLMs). In this work, we provide a perspective on Machine Unlearning (MU) in supervised learning settings, with a particular focus on Natural Language Processing (NLP) scenarios, grounded in a PRISMA-driven systematic review. We propose a multi-level taxonomy that organizes MU techniques along practical and conceptual dimensions, including exactness (exact versus approximate), unlearning granularity, guarantees, and application constraints. To complement this perspective, we run an illustrative benchmark evaluation using a standardized unlearning protocol on DistilBERT trained on a public corpus of news headlines for topic classification, contrasting the retraining gold standard with representative design-for-unlearning and approximate post hoc techniques. For completeness, we also report two oracle-assisted upper-bound baselines (distillation and scrubbing) that rely on a clean retrained reference model, and we account for their incremental cost separately. Our analysis jointly considers model utility, probabilistic quality, forgetting and privacy indicators, as well as computational efficiency. The results highlight systematic trade-offs between accuracy, computational cost, and removal effectiveness, providing practical guidance for selecting machine unlearning techniques in realistic deployment scenarios. Full article

This article presents an adaptive neural network method for the automated detection, reconstruction, and prioritisation of multi-stage criminal operations in the digital environment, aiming to protect human rights and ensure the legal security of digital evidence. The developed method combines multimodal temporal encoders, a graph module based on GNN for entity correlation, and a correlation head with a link-prediction mechanism and differentiable path recovery. Sliding time windows, logarithmic transformation of volumetric features, and pseudonymization of identifiers with the ability to utilise privacy-preserving procedures (federated learning, differential privacy) are used for data aggregation and normalisation. Unique features of the developed method include an integrated risk function combining an anomaly component and graph significance, a module for automated forensic packet generation with chain of custody recording, and a mechanism for incremental model updates. Experimental results demonstrate high diagnostic metric values (AUC ≈ 0.97, F1 ≈ 0.99 on the test dataset after balancing), robust recovery of priority paths (“path_probability” > 0.7 for top operations), and pipeline performance in PII leak prioritisation and human trafficking reconstruction scenarios. The study’s contribution lies in a practice-oriented neural network method that integrates detection, correlation, and the collection of legally applicable evidence. Full article

Sustainable urban development increasingly relies on hyperlocal environmental analytics created by smart city platforms that combine stationary and mobile sensors, Earth observations, meteorology, and land-use data. However, accurate spatio-temporal resolution can provide indirect identification and amplify cybersecurity threats. This article proposes the regulatory and technical mapping that implements the General Data Protection Regulation (GDPR) and the Network and Information Security Directive (NIS2) throughout the lifecycle of environmental data—reception, transport, storage, analytics, sharing, and publication. The methods combine doctrinal legal analysis, a review of the scope of recent research, formalized compliance modeling, modeling with synthetic city-scale datasets, expert identification, and demonstration of integrated analytics. The demonstration links deep evaluation of neural abnormalities (convolutional plus recurrent layers), short-term Fourier transformation of sensor signals, byte-to-image telemetry fingerprints, and protocol event counters, thereby tracking detection to explanatory evidence and to control actions. Deliverables include a matrix aligning lifecycle stages with GDPR principles and rights, as well as with the responsibilities of NIS2; a checklist for assessing the impact on data protection, which takes into account the risks of fairness and stigmatization; a basic set of controls for identification and access, secure design, monitoring, continuity, supplier assurance, and incident reporting; as well as a multi-layered publishing strategy that combines transparency with privacy through aggregation, delayed release, differentiated privacy budgets, and research enclaves. The visualization confirms that technical signals can be included in audit-ready reporting and automated response, while the guidelines legally clarify the relevant bases for common use cases such as air quality assurance networks, noise mapping, citizen sensor applications, and mobility and exposure modeling. The effects of the policy emphasize shared services for small municipalities, supply chain security, and ongoing review to counteract the mosaic effect. Overall, the study shows how cities can maximize environmental and social value based on environmental data, while maintaining privacy, sustainability, and equity by design. Full article

The use of technology in mobile devices and the integration of Artificial Intelligence offers a wide range of benefits and personalised services that help users perform countless activities that assist them in their daily lives, such as at work, school, and when communicating with friends and loved ones. However, this technological evolution poses significant challenges and risks in terms of user privacy, as the personal data and information that may be shared or stored must be taken into account in order to preserve the physical and psychological integrity of users. Balancing innovation and privacy is essential to maximise the benefits of AI on mobile devices while protecting the rights and security of users. This requires a comprehensive approach involving multiple stakeholders working together to create a secure, user-centred digital environment and implement security measures to preserve that data security. Full article

This study evaluates how legal and regulatory architectures shape banks’ digital transformation in Qatar relative to peer jurisdictions and isolates the regulatory components that most strongly predict observed differences in digital maturity. Employing a comparative mixed-methods design, the study links a structured legal-regulatory assessment to quantitative benchmarking of fifteen banks (five Qatar, ten international) using a Digital Maturity Index and inferential tests (descriptive statistics, independent-samples t-tests, and OLS regressions). International banks exhibit higher average digital maturity than Qatar banks, and across the sample, regulatory clarity and coherence are positively and significantly associated with digital maturity, whereas supervisory intensity alone shows no comparable effect; implementation frictions in open banking/interoperability, unified data protection, and approval timelines constrain collaboration and product rollout in Qatar. Moreover, the cross-sectional design, modest sample size, and index weighting choices limit causal inference and external validity, indicating the need for longitudinal and quasi-experimental designs to corroborate mechanisms and generalize findings. Policymakers should adopt risk-proportionate, outcomes-based rules, codify interoperable API standards, strengthen data rights and cloud/third-party governance, and establish sector-level KPIs to match supervisory expectations with bank execution and accelerate safe digitalization. Enhancements to privacy, data portability, and inclusive digital onboarding are likely to improve consumer trust, competition, and access, thereby advancing broad-based participation in digital financial services. The study integrates legal analysis with bank-level operational metrics through an analytically tractable index and a Qatar–international comparison, demonstrating the outsized role of regulatory clarity in advancing digital maturity. Full article

The rapid digitization of library resources has intensified the need for robust digital-rights management (DRM) mechanisms to safeguard copyright, control access, and preserve user privacy. Conventional DRM approaches are often centralized, prone to single-point-of-failure, and are limited in transparency and interoperability. To address these challenges, this article puts forward a decentralized DRM framework for library systems by leveraging blockchain technology and decentralized DRM-key mechanisms. An integrative review of the available research literature provides an analysis of current blockchain-based DRM library systems, their limitations, and associated challenges. To address these issues, a controlled experiment is set up to implement and evaluate a possible solution. In the proposed model, digital content is encrypted and stored in the Inter-Planetary File System (IPFS), while blockchain smart contracts manage the generation, distribution, and validation of DRM-keys that regulate user-access rights. This approach ensures immutability, transparency, and fine-grained access control without reliance on centralized authorities. Security is enhanced through cryptographic techniques for authentication. The model not only mitigates issues of piracy, unauthorized redistribution, and vendor lock-in, but also provides a scalable and interoperable solution for modern digital libraries. The findings demonstrate how blockchain-enabled DRM-keys can enhance trust, accountability, and efficiency through the development of secure, decentralized, and user-centric digital library systems, which will be of interest to practitioners charged with library IT technology management and to researchers in the wider field of blockchain applications in organizations. Full article

Objectives: This study aims to explore the facilitators and barriers in the process of using AI agents for disease management in older COPD patients. Methods: Based on the normalization process theory, a descriptive qualitative study was used to conduct semi-structured interviews with 28 older patients with COPD recruited from June to August 2025 in a Class A tertiary hospital in Wuxi, Jiangsu Province. Results: A total of 28 interviews were conducted. Four themes (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring), nine subthemes (recognition of intelligent technology;supported by policy discourse and the background of national-level projects; the creation of a family atmosphere; recommendations from HCPs; relief and social connection; new “doctor”–patient relationship and communication; eliminate the burden and return to life; benefit and value perception; right self-decision by AI) in facilitators and nine subthemes (privacy conflicts and trust deficiency; blurred boundaries of human–machine responsibility and authority; non-high-quality services are chosen reluctantly; technical anxiety; lack of motivation for continued engagement; extra burden; limitations of the physical environment; human–machine dialogue frustration; a sense of uncertainty about the future of AI) in barriers were extracted. Conclusions: This study identified key factors influencing the use of AI agents in chronic disease management in older patients with COPD. The results provide directions for improving the implementation and sustainable use of AI health technologies. Full article