User Tracking Research Articles

Improvement of a Conditional Privacy-Preserving and Desynchronization-Resistant Authentication Protocol for IoV

In Internet of Vehicles (IoV), the secure data transmission between vehicles and transportation infrastructure effectively ensures the safety and fast driving of vehicles, while authentication and key agreement protocols between vehicles and transportation infrastructure (V2I) play an important role in ensuring data security and user privacy. However, how to design a provably secure and lightweight V2I authentication protocol for IoV is a challenge. Recently, Kumar et al. proposed a conditional privacy-preserving and desynchronization-resistant authentication protocol for IoV, but we find that Kumar et al.’s scheme is vulnerable to identity guessing attacks, impersonation attacks, and a lack of session key secrecy once the attacker obtains data stored in smart card through side-channel attacks. We also point out that Kumar et al.’s protocol is vulnerable to Roadside Unit (RSU) captured attacks and lacks perfect forward secrecy. Therefore, we propose an improved V2I authentication protocol for IoV, which uses the Physical Unclonable Function (PUF) to resist RSU captured attacks, and designed a three-factor secrecy strategy to resist side-channel attacks; a conditional privacy-preserving strategy was also adopted to achieve anonymity and malicious user tracking. Furthermore, the proposed protocol is provably secure under the random oracle model and has low computation and communication costs.

Webi Draw: Unleashing Creativity with Handcrafted Air Drawings

Webi Draw: Unleashing Creativity with Handcrafted Air Drawings

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task.

Advanced systems and technologies for the enhancement of user experience in cultural spaces: an overview

Human–computer interaction, Artificial Intelligence, and the multilingualism of digital culture open up unexpected scenarios in the contemporary design of cultural spaces with the creation of artifacts in which the analogic and digital dimensions come together to enhance the experience. Sensors and devices track user movement in the real world and translate the inputs into commands through hand gestures, speech recognition, head movements, tangible interfaces, or a combination of these elements. Through theorical models, concepts and tools, the paper reports the evolution of “User Experience” applied to personalized enjoyment and use of cultural places. The functional-performance survey of tools and technologies for perception, narrative and augmented interaction revealed models that highlight the diversity and richness of tangible and intangible cultural heritage through new forms of interaction and knowledge transfer. In this direction, new technological tools make it possible to detect, track and evaluate the personalized user experience by processing or producing large amounts of data. Therefore, on one hand the paper explores the limitations dictated by data management and user privacy in using such systems and, on the other, it prefigures new scenarios for amplifying and personalizing the user experience.

The Impact of Social Media Development on Consumption Behavior of Different Income Groups in Beijing

This study explored how social media development impacts consumption patterns across income segments in Beijing. A survey examined social media usage, shopping attitudes, and platform perceptions among 111 residents. The analysis found a limited correlation between income status and social media behaviors or views. However, age and advertising effect opinions showed significant relationships. Overall social media influence and equal platform access were key factors predicting differentiated advertising receptivity. Findings imply a broad social media impact on consumption across income levels in Beijing, with differentiation driven more by age and access than income status. Despite limitations in sampling and scope, this study provides a useful baseline understanding of the intersection between rising digital consumerism and income disparity in urban China. Further research with probabilistic sampling and qualitative data will be valuable. As social media evolves rapidly, tracking usage and attitudes will remain important. This study offers an early perspective on how consumption patterns are shaped by expanding virtual communities amidst economic gaps in Chinese cities. Follow-up studies can build on these findings through expanded methodology.

Single Antenna Tracking and Localization of RIS-enabled Vehicular Users

Single Antenna Tracking and Localization of RIS-enabled Vehicular Users

Lower-Limb Myoelectric Calibration Postures for Transtibial Prostheses.

The use of an agonist-antagonist muscle pair for myoelectric control of a transtibial prosthesis requires normalizing the myoelectric signals and identifying their co-contraction signature. Extensive literature has explored the relationship between body posture and lower-limb muscle activation level using surface electromyography (EMG), but it is unknown how these relationships hold after amputation. Using a virtual tracking task, this study compares the effect of three different calibration postures (seated, standing, dynamic) on user tracking ability while in two tracking postures (seated, standing) for 18 able-bodied (AB) subjects and 9 subjects with transtibial (TT) amputation. As expected, AB subjects produced statistically significant differences in muscle activation for gastrocnemius (GAS) when seated vs. standing during calibration (p = 8.8e-4), but not for tibialis anterior (TA) (p = 0.76). TT subjects, however, showed no significant differences in GAS or TA between seated and standing (p = 0.90, 0.60 respectively). It was also determined that normalizing EMG by the global maximum signal observed (standard in biomechanic analysis) is undesirable for myoelectric control. For best general results with this framework, calibration in both seated and dynamic postures is recommended, taking the normalization information from the seated posture and the narrowest co-contraction slopes from the two.

UAV-Assisted Communications in SAGIN-ISAC: Mobile User Tracking and Robust Beamforming

UAV-Assisted Communications in SAGIN-ISAC: Mobile User Tracking and Robust Beamforming

LEO- and RIS-Empowered User Tracking: A Riemannian Manifold Approach

LEO- and RIS-Empowered User Tracking: A Riemannian Manifold Approach

CoStricTor: Collaborative HTTP Strict Transport Security in Tor Browser

HTTP Strict Transport Security (HSTS) is a widely-deployed security feature in modern web browsing. It is also, however, a potential vector for user tracking and surveillance. Tor Browser, a web browser primarily concerned with online anonymity, disables HSTS as a result of this tracking potential. We present the CoStricTor protocol which crowdsources HSTS data among Tor Browser clients. It gives Tor Browser users increased resistance to man-in-the-middle attacks without exposing them to HSTS tracking. Our protocol adapts other privacy-preserving data aggregation algorithms to share data effectively among users with strong local differential privacy guarantees. The CoStricTor protocol resists denial of service attacks by design through our innovative use of Bloom filters to represent complementary data. Our simulations show our protocol can model up to 150,000 websites, providing 10,000 upgrades to HSTS for users.

AI Chatbot Adoption in Academia: Task Fit, Usefulness and Collegial Ties

Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.

CAWAL: A novel unified analytics framework for enterprise web applications and multi-server environments

In web analytics, cloud-based solutions have limitations in data ownership and privacy, whereas client-side user tracking tools face challenges such as data accuracy and a lack of server-side metrics. This paper presents the Combined Analytics and Web Application Log (CAWAL) framework as an alternative model and an on-premises framework, offering web analytics with application logging integration. CAWAL enables precise data collection and cross-domain tracking in web farms while complying with data ownership and privacy regulations. The framework also improves software diagnostics and troubleshooting by incorporating application-specific data into analytical processes. Integrated into an enterprise-grade web application, CAWAL has demonstrated superior performance, achieving approximately 24% and 85% lower response times compared to Open Web Analytics (OWA) and Matomo, respectively. The empirical evaluation demonstrates that the framework eliminates certain limitations in existing tools and provides a robust data infrastructure for enhanced web analytics.

Classification of cyber threats for internet of things

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

Toward the flow-centric detection of browser fingerprinting

Browser fingerprinting has become a prevalent technique employed by websites for advertising and analytics. It utilizes JavaScript objects and APIs to gather traditional and non-traditional browser attributes and creates unique identifiers for online user tracking. While previous research has examined the invocation of the browser's built-in JavaScript objects and APIs to retrieve browser attribute values, it overlooks the use and flow of those values within scripts.In this paper, we define browser fingerprinting behavior as the aggregation of different types of browser attributes, and reduce the detection of browser fingerprinting to a joint analysis of the data flows of browser attribute values in JavaScript code. FProbe, our proposed framework for the flow-centric detection of browser fingerprinting, performs context-sensitive static data flow analysis on JavaScript code. Given the complexity and dynamic features of the JavaScript language, achieving soundness in static analysis of JavaScript code is extremely challenging or even impossible. Consequently, FProbe aims to be a practical and accurate tool for detecting browser fingerprinting. We implemented FProbe in Java and evaluated its performance using 4,296 fingerprinting scripts from recent work and 2,335,317 pieces of JavaScript code on 988,220 websites. FProbe achieved F-measures of 97.81% and 96.31% on these datasets, respectively. It identified browser fingerprinting behavior on 0.78% of the 988,220 websites, with the use of the toDataURL method observed in 4.26% of the fingerprinting scripts. Notably, only 72 fingerprinting scripts and 10 fingerprinting providers identified by FProbe were reported in previous work. These results highlight the effectiveness of FProbe in detecting browser fingerprinting and its complementarity to existing detection tools. Additionally, our comprehensive study demonstrates that fingerprinting with traditional browser attributes can achieve a 96.6% F-measure.

METAseen: Analyzing network traffic and privacy policies in Web 3.0 based Metaverse

Metaverse is a new emerging concept building up a virtual environment for the user using Virtual Reality (VR) and blockchain technology but introduces privacy risks. Now, a series of challenges arise in Metaverse security, including massive data traffic breaches, large-scale user tracking, analysis activities, unreliable Artificial Intelligence (AI) analysis results, and social engineering security for people. In this work, we concentrate on Decentraland and Sandbox, two well-known Metaverse applications in Web 3.0. Our experiments analyze, for the first time, the personal privacy data exposed by Metaverse applications and services from a combined perspective of network traffic and privacy policy. We develop a lightweight traffic processing approach suitable for the Web 3.0 environment, which doesn't rely on complex decryption or reverse engineering techniques.We propose a smart contract interaction traffic analysis method capable of retrieving user interactions with Metaverse applications and blockchain smart contracts. This method provides a new approach to de-anonymizing users' identities through Metaverse applications. Our system, METAseen, analyzes and compares network traffic with the privacy policies of Metaverse applications to identify controversial data collection practices. The consistency check experiment reveals that the data types exposed by Metaverse applications include Personal Identifiable Information (PII), device information, and Metaverse-related data. By comparing the data flows observed in the network traffic with assertions made in the privacy regulations of the Metaverse service provider, we discovered that far more than 49% of the Metaverse data flows needed to be disclosed appropriately.

Navigating The Complexity Overcoming Mobility Obstacles in Massive MIMO Systems with MIMO Antennas.

This research investigates the synergy between Massive Multiple Input, Multiple Output (MIMO) systems and mobility challenges, with a focus on the pivotal role played by Multiple Antenna Systems (MIMO antennas). Employing a sophisticated experimental setup featuring a 3.5 GHz Massive MIMO base station equipped with 64 MIMO antennas, mobile terminals on motorized platforms, and advanced RF measurement tools, the study explores adaptive beamforming, spatial multiplexing, and diversity techniques. Results from 100 experiments, each lasting 10 minutes, reveal that adaptive beamforming dynamically improves user tracking in response to mobility-induced channel variations. Spatial multiplexing and diversity techniques enhance throughput and reliability, especially in high user mobility scenarios. The quantitative findings align with existing literature, providing practical insights for the deployment of MIMO antennas. The study addresses limitations and proposes avenues for future research, enhancing our understanding of MIMO antenna applications and contributing to the evolution of wireless communication technologies in dynamic environments.

Contraception, Fertility Tracking, and the Limits of Medical Devices Regulation

Many technologies in mobile health (mHealth) available for private purchase and use are becoming available in smartphone app stores worldwide. Of key concern is the proliferation of fertility-related ‘femtech’ (FRF), a category of software that is increasingly being used by women, girls and people with wombs as their main contraceptive method. Contraceptives are an important part of women’s health, and like any other medicine, they are stringently regulated in the United Kingdom (UK). ‘Digital contraceptive’ software, when marketed as such, normally falls within the definition of a ‘medical device’ and is regulated by the UK’s medical devices regime. However, some apps that may be used as digital contraceptives through tracking users’ ‘fertile windows’ sit outwith this framework and thus leave their users at risk of unplanned pregnancy. Software as a medical device (SaMD) is rapidly growing beyond the vision of the current medical devices regime, even within the past few years. This paper explores, in depth, the urgent issue of regulatory inadequacy when it comes to femtech software that may be used for contraceptive purposes. Law and regulation in this field, as they are currently constituted, are ill equipped to capture the types of mHealth and personal health tracking technologies that, normatively speaking, ought to be caught by the medical devices regime. Therefore, it is posited that while FRF poses a distinct challenge for law and regulation in that FRF may cause unwanted pregnancies, the specific issue of regulatory inadequacy posed is symptomatic of a broader problem faced by the medical devices regime.

An intelligent zero trust secure framework for software defined networking

Software-defined networking (SDN) faces many of the same security threats as traditional networks. The separation of the SDN control plane and data plane makes the controller more vulnerable to cyber attacks. The conventional “perimeter defense” network security model cannot prevent lateral movement attacks caused by malicious insider users or hardware and software vulnerabilities. The “zero trust architecture” has become a new security network model to protect enterprise network security. In this article, we propose an intelligent zero-trust security framework IZTSDN for the software-defined networking by integrating deep learning and zero-trust architecture, which adopts zero-trust architecture to protect every resource and network connection in the network. IZTSDN uses a traffic anomaly detection mode CALSeq2Seql based on a deep learning algorithm to analyze users’ network behavior in real-time and achieve continuous tracking and analysis of users, restrict malicious users from accessing network resources, and realize the dynamic authorization process. Finally, the Mininet simulation platform is extended to build the simulation platform MiniIZTA supporting zero-trust architecture and the proposed security framework IZTSDN is experimentally analyzed. The experimental results show that the IZTSDN security framework can provide about 80.5% of throughput when the network is attacked. The accuracy of abnormal traffic detection reaches 99.56% on the SDN dataset, which verifies that the reliability and availability of the IZTSDN security framework are verified.

BE-TRDSS: Blockchain-Enabled Secure and Efficient Traceable-Revocable Data-Sharing Scheme in Industrial Internet of Things

As an important component of the industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semi-trusted and has structural limitations and vulnerability, which makes it necessary to realize data dynamic security sharing and malicious users tracking. In this paper, we show that most recent work on this issue is still vulnerable to security threats at first. Then a blockchain-enabled dynamic and traceable data-sharing scheme for a smart factory is proposed. Blockchain performs the user authentication and stores the ciphertext index and public keys to avoid tampering with shared data. The tracking algorithm tracks malicious users and adds them to a revocation list embedded in the ciphertext. And the authority can flexibly select domain or user revocation as required. The LSSS access policy is hidden to protect user privacy, and the cloud server uses match test algorithm to detect whether users meet hidden access policy. Additionally, online-offline encryption and outsourced decryption improve the efficiency of the scheme where the ciphertext and the pairing operations required for decryption achieve constant size. A performance analysis shows that the scheme can resist a variety of collusion attacks, and simulations show that it outperforms current schemes.

A teaching model of college ideological and political theory course based on multiuser detection algorithm

Abstract To prove that the hybrid teaching mode of ideological and political theory courses in colleges and universities is significantly helpful in improving the teaching effect of Civics and Political Science, this study builds a basic framework of the hybrid teaching mode of the course by using the smart teaching platform starting from the accurate teaching mode of information technology. For the user tracking of the smart teaching platform, the gradient tracking multi-user detection algorithm based on the multi-step proposed Newton method is used to realize the user data analysis and the convergence analysis is conducted for the GDGP-MUD algorithm and MSQN-MUD algorithm. The results show that the maximum average response time is 460ms when the number of concurrent users of the smart teaching platform is 350-400. The average performance of the experimental class under the comparative analysis is 11.41 points higher than that of the control class.