User Identification Research Articles

Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment

AbstractThe field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit interconnections. UA plays a pivotal role in synthesising coherent user profiles and delving into the intricacies of user behaviour across platforms. However, traditional approaches have encountered limitations. Singular embedding techniques have been found to fall short in fully capturing the semantic essence of user profile attributes. Furthermore, classification-based embedding methods lack definitive criteria for categorisation, thereby constraining both the efficacy and applicability of these models. This paper presents a novel unsupervised Gradient Semantic Model for User Alignment (GSMUA) for the purpose of identifying common user identities across social networks. GSMUA categorises user profile information into weak, sub, and strong gradients based on the semantic intensity of attributes. Different gradient semantic levels direct attention to literal features, semantic features, or a combination of both during feature extraction, thereby achieving a full semantic representation of user attributes. In the case of strongly semantic long texts, GSMUA employs Named Entity Recognition (ENR) technology in order to enhance the inefficient handling of such texts. Furthermore, GSMUA compensates for missing user profile attributes by utilising profile information from user neighbours, thereby reducing the negative impact of missing user profile attributes on model performance. Extensive experiments conducted on four pairs of real datasets demonstrate the superiority of our approach. In comparison to the most effective previously developed unsupervised methods, GSMUA demonstrates improvements in hit-precision ranging from 5.32 to 12.17%. When compared to supervised methods, the improvements range from 0.71 to 11.79%.

Linguistic Landscapes of Social Media Discourse: Exploring Language Practices and Identities on Jordanian Online Platforms

The present research is designed to investigate the language practices and identities of Jordanian users on the selected social media platforms of Facebook and Twitter. A total of 400 posts and tweets were selected from both social media platforms. Based on the Systemic Functional Linguistics theory, the study found that the users demonstrated various language practices on social media like code-switching, emojis, localized expressions, and identity affiliations like racism, patriotism, and feminism. Consequently, the study established that Jordanian Arabic is the most frequently-used language style, while the use of English and MSA as secondary language styles was considerably less popular. Additionally, code-switching was the most prevalent language practice. The study recommended educational policies to advocate bilingualism, while for social media, the strategies need to reflect linguistic characteristics to increase reach and engagement.

Metaverse Metamorphosis: Fintech's Evolution Catalyzing New Era of Consumer Behavior

ABSTRACTAll facets of society may enter the virtual world more quickly due to the rapid development of digital technology, which would blur the boundary between the real and the digital realms and encourage the creation of a Metaverse. In the upcoming decades, metaverse technology—which allows immersive experiences in virtual and real environments—will be the next revolutionary force in society. The Fintech industry has been one of the first to adopt it, and it is one of those that uses technology to resolve problems. The study begins by conceptualizing the Metaverse as being defined explicitly by the convergence of its essential components and its influence on the fintech sector. It further discusses how Metaverse could create value in consumer behaviour in three vicinities: social perception, user identity and advertising & consumer psychology. Finally, the study presents the various implications and future scope of this research by doing the thematic analysis to enhance our understanding of the Metaverse economy and how it influences consumer behaviour. This study provides an overview of the shift occurring in the marketplace and contemplates the various changes that it may bring, all through the perspective of consumer behaviour which can be used to create predictive models that recognize emerging trends in customer behaviour, market activities, and other aspects of the business.

Self-Sovereign Identity-Based E-Portfolio Ecosystem

In Taiwan, traditional student assessments, covering academic and extracurricular achievements, have shifted from paper to electronic portfolios (e-portfolios). However, limited trust among institutions restricts students from freely sharing and using their educational data. This paper introduces a self-sovereign identity-based infrastructure aimed at enhancing personal data security within the e-portfolio ecosystem. The proposed system includes two core components: (1) a decentralized identity chain, aligning user identities across platforms and granting users full self-sovereign control; and (2) an e-portfolio application chain to manage user interactions and access permissions within the ecosystem. A trusted educational authority also audits data sources, ensuring data integrity and reliability. This infrastructure empowers users to control who can access their data, safeguarding their security, with the identity chain preventing unauthorized access and the application chain recording authorization statuses to restrict data visibility to approved parties only.

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration.

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user's identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

Change in vaping, smoking and dual-use identities predicts quit success and cigarette usage: A prospective study of people quitting smoking with electronic cigarette support.

Electronic cigarettes (ECs) are an efficacious support for some but not all people wishing to stop using tobacco. While advice and practical support have been identified as increasing quit success, little research has explored the role of changes in smoking and EC-related social identities. A prospective study following 573 people attempting to quit smoking with EC support. Self-report measures of identification with being a smoker, non-smoker, vaper and dual user (people using ECs and tobacco products) were taken prior to the quit attempt and at a 12-week follow-up. Baseline identifications with being a smoker, non-smoker or dual user were not associated with smoking outcomes. Baseline vaper identity baseline was linked to more frequent tobacco abstinence at follow-up and lower levels of cigarette smoking. Levels of social identification at follow-up were consistently linked with outcomes, with vaper identity and non-smoking identities being protective and dual user identity being related to lower abstinence rates but decreased tobacco usage. Changes in identity over time were the most consistent predictor of outcomes. Findings have implications for smoking cessation practice, informing how and when identity-based interventions may be effective and our understanding of how identity transitions occur.

BCAS: Blockchain-based secure access and sharing scheme for EHR data

Sharing Electronic Health Record (EHR) data is critical for improving medical decision-making and emergency care. However, EHRs contain sensitive patient information, making data breaches and misuse a significant concern during sharing. To tackle this challenge, we propose the Blockchain-Based Secure Access and Sharing (BCAS) scheme, a patient-centered, secure, and efficient solution for large-scale EHR access and sharing that intelligently combines blockchain with proxy re-encryption. Four smart contracts are designed for the BCAS to achieve effective user identity registration, precise virtual identity verification, strict access control, and secure data management. The adoption of hybrid on-chain and off-chain storage structures, coupled with the four smart contracts, effectively optimizes the efficiency of the blockchain in BCAS. Proxy re-encryption further enhances data confidentiality and streamlines EHR sharing among authorized users. The security analysis indicates that BCAS can resist DDoS, spoofing, and man-in-the-middle attacks, ensuring the privacy and security of patient EHR data. The performance evaluation demonstrates that the CPU usage of smart contracts does not exceed 15%. The on-chain and off-chain storage structures require less than 30s for uploading and downloading an 800MB file. Additionally, the proxy re-encryption scheme can process a 10MB file in less than 43ms. These results indicate that BCAS offers efficient performance without compromising data security and privacy. Compared with existing blockchain-based EHR sharing systems, BCAS offers superior performance, a more comprehensive solution, and increased resistance to attacks.

Triggerpunkt-Behandlungs-Tool zur Selbsttherapie von Myogelosen im Berufsumfeld – eine Anwendungsbeobachtung

Trigger point treatment tool for self-therapy of myogelosis in the professional environment – An observational study Aim: There is a large number of occupations where workplace-associated movement patterns and/or forced postures lead to muscle tension in the shoulders and back. These tensions have a direct detrimental impact on the quality of life and capacity for work of those affected. In the medium to long term, they also promote the development of structural degeneration of the musculoskeletal system. If employees in the healthcare sector are affected, this impairs high-quality patient care. The trigger point self-treatment tool “TensionTerminator” (ErgoPhysion, Innsbruck, Austria) was developed for the self-therapy of such tension conditions in the workplace. The use of this device was evaluated as part of an application observation in six departments at Vienna General Hospital (AKH). Methods: Six departments at Vienna General Hospital from the areas of surgery, laboratory medicine, internal medicine and radiology were each provided with a “TensionTerminator” device for independent use for 4 months between May 2023 and March 2024. A contact person in each department was trained in the use of the device and how to handle the documentation. As part of the observation, users were asked to independently enter their user ID, the duration of use and their subjective assessment of the effect (“better”/”worse”/”no change”) in a list each time they used the device. Results: During the test period, a total of 430 uses by 145 users were documented in the six departments, i.e. an average of almost 3 uses per person. The average application duration was 6.5 minutes. In 85.1% of all uses, the patient’s condition after use was described as “better”, and in 14% of all cases as “no change”. One of the 430 uses received a negative rating (“worse”). Conclusion: The trigger point self-treatment tool “TensionTerminator” has proven to be an effective device against workplace-associated muscle tension. It was received very well in the hospital setting and was used regularly. Keywords: workplace health promotion – back pain – myogelosis – myofascial trigger points

User Identification via Touch-screen Button Operation for Smart Home

User Identification via Touch-screen Button Operation for Smart Home

From Social Media to Public Health: The Role of Identity Recognition in Post-Pandemic Information Dissemination

With social media becoming an essential platform for public health information communication, the impact of identity on the effectiveness of information dissemination in social media has received increasing attention. This study aimed to explore the relationship between identity factors in social media and the effectiveness of public health news dissemination. Different social media user identities have different effects and concerns on public information communication, and the identity backgrounds of bloggers have different perspectives on public health information communication. The findings help to identify the effects of different identities in public health communication and provide possible research ideas and perspectives for future researchers.

UeLP: accurate user linkage across social platforms against location errors

Abstract User linkage across social platforms can connect the accounts of the same user across different social networks, which is crucial for the identification of users’ multiple social identities and cross-platform association analysis. Cross-platform user linkage based on location is a typical method in current research. These methods typically rely on check-in data to calculate user similarity. However, different from check-in location, the location data obtained from instant messaging social platforms may contain random errors, leading to low accuracy of user linkage of such methods. To solve this problem, this paper proposes an accurate user linkage method across social platforms against location errors. First, unlike existing methods that employ fixed-size grids, this paper uses a multi-grained spatio-temporal grid to organize data, in order to accurately extract user features from error locations. Then, by extracting coarse-grained movement pattern features from user trajectories, candidate users are filtered out, and a small subset of candidate uses is generated to effectively reduce the search space. Next, we establish a weight model based on grid contribution and motion sequence similarity to extract location and temporal features with stronger user orientation. Finally, according to the weight model, the weighted cluster center distance of trajectories is used to calculate the similarity between two different user trajectories. The user with the highest similarity is selected from the candidate subset to complete the user linkage. The extensive experiments are conducted on six public datasets containing 115 866 trajectories and a self-built dataset with 5358 trajectories. The results show the following: compared with the four existing typical location-based methods $k$-BCT, GS, TF-IDF, and TF-IWF, the accuracy Acc@1 is improved by an average of 33%, 44.94%, 15.2%, and 14.55%, respectively, and the accuracy Acc@3 is improved by 30.52%, 34.67%, 13.84%, and 13.19%, respectively.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

User-wise perturbations for user identity protection in EEG-based BCIs.

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.

Railway security checks at the border: between intrusive security technologies and fundamental traveller rights.

European railway borders are facing a particular exposure to security threats and need a delicate balance between securitization and free movement, especially amid globalisation, the current geopolitical landscape and increased migrant flows. For example, the war in Ukraine illustrated the challenges experienced at the Eastern EU borders by the refugee migration surge in early 2022. This exploratory focuses on the European border security control process from the rail border perspective. It encompasses a descriptive synthesis of the lessons learned from the UIC Refugee Task Force as well as insights from the ongoing EU-funded Horizon Europe project ODYSSEUS (Unobtrusive Technologies for Secure and Seamless Border Crossing for Travel Facilitation). Project ODYSSEUS aims to support the security and integrity of the European space, reduce illegal movements of people and goods across EU borders, facilitate travelling for citizens all while protecting fundamental rights of travellers. The project will test a combination of multi-behavioural and General Data Protection Regulation (GDPR) compliant biometric user identity verification tools, allowing, when possible, citizens to cross EU border without any interruption or queue. Further, novel luggage and baggage checks will allow citizens' vehicles and cargos to be remotely checked at land borders to speed up the border check processes in a secure and reliable manner. The project will run three pilot tests at road, rail and water borders. In this paper, we analyse the implementation of project's technologies in the rail border crossing pilot test and discuss the implications for the actors involved in the process of railway border crossing (e.g., border authorities, railway operators and railway travellers).

Using 3D Hand Pose Data in Recognizing Human–Object Interaction and User Identification for Extended Reality Systems

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems.

In-situ cured gel polymer/ecoflex hierarchical structure-based stretchable and robust TENG for intelligent touch perception and biometric recognition

Gel-based sensors for next generation touch panels have been acknowledged for their exceptional sensitivity and flexibility. However, these sensors typically depend on a metal grid connection, which is susceptible to structural deformation under heavy stress applications and necessitates external power. Here, we report a novel in-situ cured gel polymer electrode-based triboelectric nanogenerator (GPE-TENG) that is stretchable, semi-transparent, and durable, designed to enable a self-powered touch panel for intelligent touch perception. The in-situ curing of the hierarchical structure of the ionic polymer gel encapsulated within the ecoflex ensures robust adhesion of the ionic conductive polymer gel (PEO/LiTFSI) to the ecoflex layers, addressing the issue of delamination in TENG components under mechanical stress. As a result, the GPE-TENG demonstrates high durability, enduring under stretching of approximately 375 % and sustaining heavy mechanical deformations (under folding, twisting, and rolling) over a long period (approximately 2 months) without loss of functionality. Remarkably, the GPE-TENG exhibits outstanding energy harvesting capabilities with a peak power density of 0.36 W m-2. Notably, the GPE-TENG generates electrical signals through simple device stretching, thus serving as a self-powered wearable sensor for human activity monitoring. Moreover, a 9-digital arrayed (3 × 3) flexible, semi-transparent, and self-powered touch panel based on the GPE-TENG shows multifunctionality, including touch track/pattern recognition (i.e. touch and sliding mode) and a highly accurate (∼98 %) deep learning assisted smart biometric system for user identification.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life, but it also increases the security problem. Information security has become one of the severe challenges faced by people in the digital age. Currently, the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology, the risk of large-scale data transmission, the potential vulnerabilities introduced by emerging technology, and the dilemma of user identity verification. This paper analyzes the frontier challenges of communication technology and computer network security, and puts forward corresponding solutions, hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

WiCAM2.0: Imperceptible and Targeted Attack on Deep Learning based WiFi Sensing

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose WiCAM2.0 to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods like FGSM and PGD to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.

“I’m committed, not addicted.”: College students’ identity and communication about vaping

Objective Vaping is a relatively new phenomenon, and trends in social vaping behaviors are developing. The current study describes and explains how communication surrounding vaping shapes different aspects of a college student’s identity. Participants Twenty-seven male and female college students at a large Midwestern university were recruited to participate in five focus groups. Methods Focus group findings were interpreted through the lens of the communication theory of identity. Results College students’ communication of identity was manifest in the personalization of vaping devices, selling vaping devices as a relational activity, the social aspects of vaping addiction, the facetious language surrounding vaping, and perceptions of vaping tricks as “cringe.” Conclusions The findings revealed narrow perceptions of acceptable identities for users of vaping devices. Participants avoided labels of “being addicted” and leveraged discursive strategies such as humor, facetious language, othering, and re-framing behavior to fit a desirable identity.