Search Results (1,033)

The CPace protocol (Internet-Draft:draft-irtf-cfrg-cpace-14) is a password-authenticated key exchange optimized for simplicity. In particular, it involves only two messages exchanged in an arbitrary order. CPace combines a simple and elegant design with privacy guarantees obtained via strict mathematical proofs. In this paper, we go further and analyze its resilience against malicious cryptography implementations. While the clever design of CPace immediately eliminates many kleptographic techniques applicable to many other protocols of this kind, we point to the remaining risks related to kleptographic setups. We show that such attacks can break the security and privacy features of CPace. Thereby, we point to the necessity of very careful certification of the devices running CPace, focusing in particular on critical threats related to random number generators. Full article

Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics. Full article

Volumetric video streaming holds transformative potential for telemedicine, enabling immersive remote consultations, surgical training, and real-time collaborative diagnostics. However, transmitting sensitive patient data (e.g., 3D medical scans, surgeon head/gaze movements) raises critical privacy risks, including exposure of biometric identifiers and protected health information (PHI). To address the above concerns, we propose SecureTeleMed, a dual-track encryption scheme tailored for volumetric video based telemedicine. SecureTeleMed combines viewport obfuscation and region of interest (ROI)-aware frame encryption to protect both patient data and clinician interactions while complying with healthcare privacy regulations (e.g., HIPAA, GDPR). Evaluations show SecureTeleMed reduces privacy leakage by 89% compared to baseline encryption methods, with sub-50 ms latency suitable for real-time telemedicine applications. Full article

While Internet privacy is a subjective term that is challenging to define, describe, and quantify, assessing the level of privacy provided by data processors offering services over the Internet is essential for detecting privacy flaws and enabling continuous improvement. Moreover, assessing Internet privacy is fundamental for estimating the risk of personal data disclosure, the degree of compliance with privacy regulations, and the effectiveness of implemented protection mechanisms. Remarkably, the absence of a standardized criterion for this assessment has led to the proliferation of diverse heuristic techniques applied with different approaches. In this paper, we conduct an in-depth analysis and introduce a novel taxonomy for categorizing existing heuristic techniques to assess Internet privacy. Moreover, we scrutinize various protection mechanisms designed to enhance users’ privacy. We cover this broad topic across all domains of application and levels of automation, considering all relevant papers regardless of publication year, ultimately providing a comprehensive review of this important field of knowledge. Leveraging our proposed classification framework, we systematically organize and categorize 160 papers carefully selected from 934 candidates, elucidating existing gaps and challenges while foreseeing future research directions. Overall, our findings reveal that most studies predominantly rely on information measurement methods for assessing Internet privacy. Although most heuristic techniques are based on automatic mechanisms, they are applied with a clear focus on the traditional use of Internet services through a web browser, demanding more research efforts for other domains. The development of new technologies that incorporate privacy-by-default and include telemetry modules in their architectures will be essential for assessing and enhancing users’ privacy when delivering services over the future Internet. Full article

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article

Artificial Intelligence (AI) profoundly shapes adolescents’ digital experiences, presenting both developmental opportunities and risks related to privacy and psychological well-being. This study investigates first the possible generational gap between adolescents and their parents in AI use and trust, and then the associations between the Trait Emotional Intelligence (trait EI), parenting styles, perceived social support, and parental involvement on adolescents’ use and trust in AI-based technologies. Participants were 170 adolescents (aged 13–17) and 175 parents from southern Italy, who completed standardized questionnaires assessing parenting styles, Trait Emotional Intelligence (Trait EI), social support, digital literacy, and use and trust in AI. Adolescents used AI more frequently than parents, especially for school- or work-related support and were more likely to seek behavioral advice from AI. They also showed higher trust in AI data security and the quality of behavioral advice than parents. Moreover, greater trait EI and more authoritative (vs. authoritarian) parenting were associated with less frequent AI use and lower use and trust in AI. In 47 matched parent–adolescent dyads, cluster analysis identified Balanced Users (higher trait EI, authoritative parenting, stronger support, cautious AI use) and At-Risk Users (lower trait EI, authoritarian parenting, lower support, heavier and more trusting AI use) Despite no causal inferences can be drawn due to the correlational nature of the data, the results suggested the importance of considering adolescents’ trait EI and authoritative parenting practices in supporting balanced and critical digital engagement, highlighting the concept of a “digital secure base” as essential for navigating the evolving digital landscape. Full article

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges such as algorithmic bias, data privacy, and teacher adaptation remain. This paper proposes a responsible AI integration framework, emphasizing educator–technologist collaboration, professional development, and ethical governance. Addressing these concerns requires robust policies and continued research to maximize benefits while minimizing risks in AI-enhanced education. Full article

Human pose estimation (HPE) in privacy-sensitive environments such as healthcare facilities and smart homes demands non-visual sensing solutions. Millimeter-wave (mmWave) radar emerges as a promising alternative, yet its development is hindered by the scarcity of high-fidelity datasets with accurate annotations. This paper introduces mmFree-Pose, the first dedicated mmWave radar dataset specifically designed for privacy-preserving HPE. Collected through a novel visual-free framework that synchronizes mmWave radar with VDSuit-Full motion-capture sensors, our dataset covers 10+ actions, from basic gestures to complex falls. Each sample provides (i) raw 3D point clouds with Doppler velocity and intensity, (ii) precise 23-joint skeletal annotations, and (iii) full-body motion sequences in privacy-critical scenarios. Crucially, all data is captured without the use of visual sensors, ensuring fundamental privacy protection by design. Unlike conventional approaches that rely on RGB or depth cameras, our framework eliminates the risk of visual data leakage while maintaining high annotation fidelity. The dataset also incorporates scenarios involving occlusions, different viewing angles, and multiple subject variations to enhance generalization in real-world applications. By providing a high-quality and privacy-compliant dataset, mmFree-Pose bridges the gap between RF sensing and home monitoring applications, where safeguarding personal identity and behavior remains a critical concern. Full article

Aim: This study extends existing research by focusing specifically on the perceptions of hotel employees, a crucial yet often overlooked group of stakeholders in the adoption of new technologies within the hotel sector. The objective of this research is to investigate the perceptions of hotel employees in Hong Kong regarding the implementation of Metaverse hotels. This study emphasizes their evaluations of the strengths, weaknesses, opportunities, and threats associated with these establishments through a SWOT analysis. Ultimately, the study aims to provide recommendations for addressing technological challenges, supporting employees during the transition, and facilitating adaptation across the industry. Design/Methodology/Approach: A convenience and purposive sampling method is employed to investigate 20 participants, comprising hotel staff from various departments in the Metaverse hotel in Hong Kong. This study adopts a qualitative research design, utilizing semi-structured interviews to gather in-depth insights into the perceptions of the Metaverse hotel among these employees. Purposive sampling ensures that participants have relevant experience and familiarity with VR/AR technologies. Interviews, each lasting 45–60 min, were conducted in person, with informed consent obtained beforehand. Findings: The exploration of hotel employees’ perceptions of Metaverse hotels in Hong Kong underscores the innovative potential of these establishments to enhance operational efficiency and guest engagement, while also offering new training opportunities and streamlining daily tasks. However, employees express concerns about the potential erosion of personal interactions, which are crucial to the hospitality experience, and foresee significant technical and integration challenges. Despite these drawbacks, Metaverse hotels present distinctive opportunities for market differentiation, appealing to tech-savvy guests and generating new revenue streams that contribute to industry growth. Nonetheless, potential threats such as guest skepticism and challenges in industry adaptation highlight the necessity for cautious implementation and robust privacy measures. Balancing these aspects—strengths, weaknesses, opportunities, and threats—will be pivotal for the successful integration of Metaverse technologies into the hotel industry. Theoretical/Practical Implications: Participants recognized that the Metaverse hotel could offer various potential benefits for both employees and businesses, such as enhanced operational efficiencies and new opportunities for guest engagement. Understanding the perceptions of hotel staff towards the Metaverse carries significant real-world implications for shaping policies, practices, and technologies that facilitate its operational success and market acceptance. Leveraging these insights enables the optimization of Metaverse’s advantages while mitigating associated risks and drawbacks. This study advances existing research by focusing specifically on the perceptions of hotel employees, a crucial yet often neglected group of stakeholders in the adoption of new technologies within the hospitality sector. By understanding the perspectives of hotel employees, this research provides valuable insights into the practical challenges and benefits of implementing Metaverse technologies in the hotel industry. Originality/Value: The Metaverse hotel is still relatively new and evolving, making it crucial to conduct research to understand how hotel staff perceive it. However, there is limited research specifically focusing on the perceptions of hotel employees regarding Metaverse hotels. This gap highlights the need for a comprehensive investigation into how employees perceive the strengths, weaknesses, opportunities, and threats of implementing the Metaverse in hotels. Full article

Recent advancements in emerging technologies such as IoT and AI have driven digital innovation, while also accelerating the sophistication of cyberattacks and expanding the attack surface. In particular, inter-state cyber warfare, sophisticated ransomware threats, and insider-led personal data breaches have emerged as significant new security risks. In response, this study proposes a Privacy-Aware Integrated Log System model developed to mitigate diverse security threats. By analyzing logs generated from personal information processing systems and security systems, integrated scenarios were derived. These scenarios are designed to defend against various threats, including insider attempts to leak personal data and the evasion of security systems, enabling scenario-based contextual analysis that goes beyond simple event-driven detection. Furthermore, the Analytic Hierarchy Process (AHP) was applied to quantitatively assess the relative importance of each scenario, demonstrating the model’s practical applicability. This approach supports early identification and effective response to personal data breaches, particularly when time and resources are limited by focusing on the top-ranked scenarios based on relative importance. Therefore, this study is significant in that it goes beyond fragmented log analysis to establish a privacy-oriented integrated log system from a holistic perspective, and it further validates its operational efficiency in field applications by conducting an AHP-based relative importance evaluation. Full article

Background: The genomic era has transformed not only the tools of medicine but the very logic by which we understand health and disease. Whole Exome Sequencing (WES), Clinical Exome Sequencing (CES), and Whole Genome Sequencing (WGS) have catalyzed a shift from Mendelian simplicity to polygenic complexity, from genetic determinism to probabilistic interpretation. This epistemological evolution calls into question long-standing notions of causality, certainty, and identity in clinical genomics. Yet, as the promise of precision medicine grows, so too do the tensions it generates: fragmented data, interpretative opacity, and the ethical puzzles of Variants of Uncertain Significance (VUSs) and unsolicited secondary findings. Results: Despite technological refinement, the diagnostic yield of Next-Generation Sequencing (NGS) remains inconsistent, hindered by the inherent intricacy of gene–environment interactions and constrained by rigid classificatory systems like OMIM and HPO. VUSs (neither definitively benign nor pathogenic) occupy a liminal space that resists closure, burdening both patients and clinicians with uncertainty. Meanwhile, secondary findings, though potentially life-altering, challenge the boundaries of consent, privacy, and responsibility. In both adult and pediatric contexts, genomic knowledge reshapes notions of autonomy, risk, and even personhood. Conclusions: Genomic medicine has to develop into a flexible, morally sensitive paradigm that neither celebrates certainty nor ignores ambiguity. Open infrastructures, dynamic variant reclassification, and a renewed focus on interdisciplinary and humanistic approaches are essential. Only by embracing the uncertainty intrinsic to our biology can precision medicine fulfill its promise, not as a deterministic science, but as a nuanced dialogue between genes, environments, and lived experience. Full article

Prolonged sedentary behavior in office environments is a key risk factor for musculoskeletal disorders and metabolic health issues. While workplace stretching interventions can mitigate these risks, effective monitoring solutions are often limited by privacy concerns and constrained sensor placement. This study proposes a ceiling-mounted ultra-wideband (UWB) radar system for privacy-preserving classification of working and stretching postures in office settings. In this study, data were collected from ten participants in five scenarios: four posture classes (seated working, seated stretching, standing working, standing stretching), and empty environment. Distance and Doppler information extracted from the UWB radar signals was transformed into modality-specific images, which were then used as inputs to two classification models: ConcatFusion, a baseline model that fuses features by concatenation, and AttnFusion, which introduces spatial attention and convolutional feature integration. Both models were evaluated using leave-one-subject-out cross-validation. The AttnFusion model outperformed ConcatFusion, achieving a testing accuracy of 90.6% and a macro F1-score of 90.5%. These findings demonstrate the effectiveness of a ceiling-mounted UWB radar combined with attention-based modality fusion for unobtrusive office posture monitoring. The approach offers a privacy-preserving solution with potential applications in real-time ergonomic assessment and integration into workplace health and safety programs. Full article

With the widespread deployment of mobile imaging sensors and smart devices, the risk of image privacy leakage is increasing daily. Protecting sensitive information in captured images has become increasingly critical. Existing image privacy protection measures usually rely on manual blurring and occlusion, which are inefficient, prone to omitting privacy information, and have an irreversible impact on the usability and quality of images. To address these challenges, this paper proposes TSLEPS (Two-Stage Localization and Erasure method for Privacy protection in Sensor-captured images). TSLEPS adopts a two-stage framework comprising a privacy target detection sub-model and a privacy text erasure sub-model. This method can accurately locate and erase the private text areas in images while maintaining the visual integrity of the images. In the stage of detecting privacy targets, an inverted residual attention mechanism is designed and combined with a generalized efficient aggregation layer network, significantly improving privacy target detection accuracy. In the stage of privacy text erasure, a texture-enhanced feature attention mechanism is proposed with an adversarial generative network for the erasure task to achieve efficient erasure of privacy texts. Moreover, we introduce the half-instance normalization block to reduce the computational load and inference time so that it can be deployed on resource-constrained mobile devices. Extensive experiments on multiple public real-world privacy datasets demonstrate outstanding performance, with privacy target detection achieving 97.5% accuracy and 96.4% recall, while privacy text erasure reaches 38.2140 dB PSNR and 0.9607 SSIM. TSLEPS not only effectively solves the privacy protection challenges in sensor-captured images through its two-stage framework, but also achieves breakthrough improvements in detection accuracy, erasure quality, and computational efficiency for resource-constrained devices. Full article

Telemedicine has transformed obstetrics and gynecology (OB/GYN), accelerated by the COVID-19 pandemic. This study aims to synthesize evidence on the adoption, effectiveness, barriers, and technological innovations of telemedicine in OB/GYN across diverse healthcare settings. This scoping review synthesized 63 peer-reviewed studies (2010–2023) using PRISMA-ScR guidelines to map global applications, outcomes, and challenges. Key modalities included synchronous consultations, remote monitoring, AI-assisted triage, tele-supervision, and asynchronous communication. Results demonstrated improved access to routine care and mental health support, with outcomes for low-risk pregnancies comparable to in-person services. Adoption surged >500% during pandemic peaks, stabilizing at 9–12% of services in high-income countries. However, significant disparities persisted: 43% of rural Sub-Saharan clinics lacked stable internet, while socioeconomic, linguistic, and cultural barriers disproportionately affected vulnerable populations (e.g., non-English-speaking, transgender, and refugee patients). Providers reported utility but also screen fatigue (41–68%) and diagnostic uncertainty. Critical barriers included fragmented policies, reimbursement variability, data privacy concerns, and limited evidence from conflict-affected regions. Sustainable integration requires equity-centered design, robust policy frameworks, rigorous longitudinal evaluation, and ethically validated AI to address clinical complexity and systemic gaps. Full article

Digital transformation in healthcare has introduced data privacy challenges, as hospitals struggle to protect patient information while adopting digital technologies such as AI, IoT, and cloud more rapidly than ever before. The adoption of powerful third-party Machine Learning as a Service (MLaaS) solutions for disease prediction has become a common practice. However, these solutions offer significant privacy risks when sensitive healthcare data are shared externally to a third-party server. This raises compliance concerns under regulations like HIPAA, GDPR, and Australia’s Privacy Act. To address these challenges, this paper explores a decentralized, privacy-preserving approach to train the models among multiple healthcare stakeholders, integrating Federated Learning (FL) with Homomorphic Encryption (HE), ensuring model parameters remain protected throughout the learning process. This paper proposes a novel Homomorphic Encryption-based Adaptive Tuning for Federated Learning (HEAT-FL) framework to select encryption parameters based on model layer sensitivity. The proposed framework leverages the CKKS scheme to encrypt model parameters on the client side before sharing. This enables secure aggregation at the central server without requiring decryption, providing an additional layer of security through model-layer-wise parameter management. The proposed adaptive encryption approach significantly improves runtime efficiency while maintaining a balanced level of security. Compared to the existing frameworks (non-adaptive) using 256-bit security settings, the proposed framework offers a 56.5% reduction in encryption time for 10 clients and 54.6% for four clients per epoch. Full article