User Tracking Research Articles

MAC Address Classification in Privacy Issue Using Gaussian Naïve Bayes

There have been several initiatives within standards committees to overcome privacy issues, including user tracking activity based on Media Access Control (MAC) addresses. The implementation of randomized MAC addresses on captive portals, with user-specific connection limits to address privacy concerns, introduces some problems. To address this issue, device removal based on OUI classification was proposed. Connection data taken from the RADIUS server were divided into two distinct classes, either random or not. Gaussian Naïve Bayes was utilized to classify the data with 16 distinct thresholds, and the solution with the highest accuracy was selected. The research produced results showing that all classifications had an accuracy above 96%. Values of 6 and 50% for Mac address thresholds and random percentage thresholds gave the highest accuracy of 98.1139%. This indicates that random Mac address classification in the real world can be done using the result.

Young athletes’ mental well-being is associated with smartphone social networking application usage and moderated by performance level and app type

Smartphones and social media have become an integral part of human daily life and they influence mental well-being. These accounts have been researched in the general population, but not in athletes. This is critical given enhanced physical and emotional stressors in athletes versus non-athletes. Therefore, we here studied intra- and interindividual relationships of four social media sites (WhatsApp, TikTok, Instagram, and Snapchat) with mood, stress, recovery, and sleep using log-based media usage tracking as an objective measure in 53 competitive athletes aged 12–27 years. Multilevel modeling revealed that intraindividual TikTok usage negatively predicted sleep (β = −0.10, p = 0.040) and recovery (β = −0.15, p < 0.002), and positively predicted stress (β = 0.12, p = 0.018). Interindividual Instagram usage predicted feelings of calmness (β = 0.27, p = 0.029) and valence (β = 0.20, p = 0.043). Intriguingly, competition level moderated the usage outcomes of all four apps: Athletes on low performance levels showed lower mental well-being when using social media longer, yet in national and international athletes the strength of these relationships was weaker or even reversed. Our study unravels social media usage associations with mental well-being to vary as a function of performance level and provides findings on intra- and interindividual effects of critical importance to inform future population-tailored and expedient interventions.

Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy

Behind everyday websites, a hidden shadow world tracks the behavior of Internet users. Web tracking analyzes online activity based on collected data and delivers content tailored to users’ interests. It gathers vast amounts of information for various purposes, ranging from sensitive personal data to seemingly minor details such as IP addresses, devices, browsing histories, settings, and preferences. While Web tracking is largely a legitimate technology, the increase in illegal user tracking, data breaches, and the unlawful sale of data has become a growing concern. As a result, the demand for technologies that can detect and prevent Web trackers is more important than ever. This paper provides an overview of Web tracking technologies, relevant research, and website measurement tools designed to identify web-based tracking. It also explores technologies for preventing Web tracking and discusses potential directions for future research.

A traceable and revocable decentralized attribute-based encryption scheme with fully hidden access policy for cloud-based smart healthcare

Smart healthcare is an emerging technology for enabling interaction between patients and medical personnel, medical institutions, and medical devices utilizing advanced Internet of Things (IoT) technologies. It has attracted significant attention from researchers because of the convenience of storing and sharing electronic medical records (EMRs) in the cloud. Given that a patient’s EMR contains sensitive individual information, it must be encrypted before uploading it to the cloud. As a countermeasure for data confidentiality and fine-grained access control, the Ciphertext Policy Attribute-Based Encryption (CP-ABE) technique is proposed, which helps manipulate private personal data without explicit authorization. However, most CP-ABE schemes use a centralized mechanism which may lead to performance bottlenecks and single-point-of-failure issues. They will also be at risk of key abuse and privacy breaches in smart healthcare applications. To this end, in this paper, we investigate a traceable and revocable decentralized attribute-based encryption scheme with a fully hidden access policy (TR-HP-DABE). Firstly, to overcome the issues of user privacy leakage and single-point-of-failure, a fully hidden access policy is established for multiple attribute authorities. Secondly, to prevent key abuse, the proposed TR-HP-DABE can achieve the tracking and revocation of malicious users by using Key Encryption Key (KEK) trees and updating the partial ciphertext. Furthermore, the online/offline encryption and verifiable outsourced decryption are applied to improve its efficiency in practical smart healthcare. According to our analysis, the security and traceability of TR-HP-DABE can be proved. Finally, the performance evaluation of TR-HP-DABE is more effective than some existing typical ones.

Cross-Domain Learning Framework for Tracking Users in RIS-Aided Multi-Band ISAC Systems With Sparse Labeled Data

Cross-Domain Learning Framework for Tracking Users in RIS-Aided Multi-Band ISAC Systems With Sparse Labeled Data

The main ways to improve the effectiveness of electronic document management protection

Information resources and information systems are among the main protected elements in all spheres of life of modern enterprises [11-15]. Today, the means of negative information impact on these elements are actively developing, the counteraction of which requires extensive diverse research and development of relevant concepts, programs for organizing specific work in the field of creating tools, methods and techniques for ensuring information security. Illegal distribution of digital documents by authorized but unscrupulous users is a growing threat to the confidentiality of organizations, which a document management system using only encryption methods cannot fully cope with. This problem occurs together with access to a digital document by unauthorized users. In this article, as a countermeasure to protect digital documents from these two threats, a new approach is proposed that carefully combines encryption and fingerprinting methods at certain stages of the digital document lifecycle. Within this scenario, a full analysis was carried out, in which the main constituent elements, their interaction, data flow and the provision of security services were identified. This protection system guarantees a sufficient level of security, since it uses standard cryptographic algorithms and recommended key sizes. The purpose of this approach is to provide information security services such as confidentiality, integrity, authentication, non-repudiation and user tracking, thereby protecting digital documents throughout their entire life cycle.

A blockchain-based architecture for tracking and remunerating fast frequency response

The increasing penetration of renewable sources introduces new challenges for power systems’ stability, especially for isolated systems characterized by low inertia and powered through a single diesel power plant, such as it happens in small islands. For this reason, research projects, such as the BLORIN project, have focused on the provision of energy services involving electric vehicles owners residential users to mitigate possible issues on the power system due to unpredictable generation from renewable sources. The residential users were part of a blockchain-based platform, which also the Distributors/Aggregators were accessing. This paper describes the integrated framework that was set up to verify the feasibility and effectiveness of some of the methodologies developed in the BLORIN project for fast frequency response in isolated systems characterized by low rotational inertia. The validation of the proposed methodologies for fast frequency response using Vehicle-to-Grid or Demand Response programs was indeed carried out by emulating the dynamic behavior of different power resources in a Power Hardware-in-the-Loop environment using the equipment installed at the LabZERO laboratory of Politecnico di Bari, Italy. The laboratory, hosting a physical microgrid as well as Power Hardware-in-the-Loop facilities, was integrated within the BLORIN blockchain platform. The tests were conducted by assuming renewable generation development scenarios (mainly photovoltaic) and simulating the system under the worst-case scenarios caused by reduced rotational inertia. The experiments allowed to fully simulate users’ interaction with the energy system and blockchain network reproducing realistic conditions of tracking and remuneration of users’ services. The results obtained show the effectiveness of the BLORIN platform for the provision, tracking and remuneration of grid services by electric vehicles and end users, and the benefits that are achieved in terms of reducing the number of diesel generating units that need to be powered on just to provide operational reserve due to the penetration of renewable sources, resulting in fuel savings and reduced emissions.

The Impact of Data Privacy on Users' Smartphone App Adoption Decisions

Rich user information is gained through user tracking and power mobile smartphone applications. Apps thereby become aware of the user and their context, enabling intelligent and adaptive applications. However, such data poses severe privacy risks. Although users are only partially aware of them, awareness increases with the proliferation of privacy-enhancing technologies. How privacy literacy and raising privacy concerns affect app adoption is unclear; however, we hypothesize that it leads to a lower adoption rate of data-heavy smartphone apps, as non-usage often is the user's only option to protect themselves. We conducted a survey (N=100) to investigate the relationship between privacy-relevant app- and publisher characteristics with the users' intention to install and use it. We found that users are especially critical of contentful data types and apps with rights to perform actions on their behalf. On the other hand, the expectation of a productive benefit induced by the app can increase the app-adoption intention. Our findings show which aspects designers of privacy-enhancing technologies should focus on to meet the demand for more user-centered privacy.

Anonymous Access System with Limited Number of Uses in a Trustless Environment

This article proposes a novel method for managing usage counters within an anonymous credential system, addressing the limitation of traditional anonymous credentials in tracking repeated use. The method takes advantage of blockchain technology through Smart Contracts deployed on the Ethereum network to enforce a predetermined maximum number of uses for a given credential. Users retain control over increments by providing zero-knowledge proofs (ZKPs) demonstrating private key possession and agreement on the increment value. This approach prevents replay attacks and ensures transparency and security. A prototype implementation on a private Ethereum blockchain demonstrates the feasibility and efficiency of the proposed method, paving the way for its potential deployment in real-world applications requiring both anonymity and usage tracking.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

Low-Complexity Mobile User Tracking in Quantized mmWave MIMO Systems

Low-Complexity Mobile User Tracking in Quantized mmWave MIMO Systems

Tracking Usage Patterns of a Mobile Application for Dementia Risk Assessment in Elderly Individuals.

Among elderly individuals, dementia stands as a prominent contributor to disability. However, multidomain lifestyle interventions have emerged as a promising approach for enhancing cognition. Taking a progressive step, integrating digital elements such as a mobile intervention application for elderly participants becomes crucial. Hence, it's crucial to identify the usage patterns of intervention features. Our aim was to implement a usage tracking mechanism and analyze usage trends and feature preferences over the initial six months of a clinical trial across various clinical sites, comparing intervention and control groups. Our findings reveal the differences in usage between the groups and highlight the most used features.

TCLNet: Turn-level contrastive learning network with reranking for dialogue state tracking

Dialogue state tracking (DST) plays a crucial role in task-oriented dialogue systems, as it interprets and tracks user intentions throughout the dialogue. The accuracy of DST directly impacts system efficiency and user experience. While existing generation-based DST models have shown promising results, effectively modelling long dialogue sequences remains a challenge. Furthermore, the greedy search decoding strategy employed by most previous works for speed is suboptimal because the probability of ground truth tokens is not always the highest. In this study, we propose a novel learning framework that addresses these issues by incorporating turn-level contrastive learning and a reranking module. Specifically, we adopt turn-level contrastive learning to progressively rectify the intermediate states of lengthy dialogues by contrasting data points at a finer granularity. Additionally, the integration of the reranking module empowers the model to make more reliable decisions at each time step by further considering alternative tokens with high probabilities. Experimental results demonstrate that our approach consistently enhances the model’s capability to handle long dialogue sequences and achieves superior performance over strong baselines on both the MultiWOZ 2.1 and MultiWOZ 2.2 datasets.

Streamlined multi-scenario revocation method leveraging blockchain and auxiliary trees

Access revocation is a fundamental aspect of modern information systems, ensuring that data remains secure and authorized personnel have appropriate access rights. However, existing access revocation methods address only one type of scenario, offering either partial or complete revocation functionalities but not both, leading to limitations in flexibility and effectiveness. This paper introduces a novel approach called streamlined multi-scenario revocation method (SMSRM) that combines block chain technology and auxiliary trees to streamline the process of multi-scenario access revocation. The SMSRM method defines two separate revoke request formats for partial and complete revocation. Auxiliary trees are used to keep track of non-revoked users, which is very important during the revocation process. In addition, the proposed method utilizes a block chain to record each and every revocation-related operation to provide forward secrecy. Through a comparative analysis, we evaluate the performance of our approach against existing methods. The results highlight that our method performs better in terms of response time and various performance metrics.

TSANet: Forecasting traffic congestion patterns from aerial videos using graphs and transformers

Forecasting traffic congestion patterns in lane-less traffic scenarios is a complex task because of the combination of high & irregular vehicle densities, fluctuating speeds, and the presence of environmental obstacles. Existing techniques like vehicle counting and density prediction, which successfully estimate congestion in lane-based traffic, are unsuitable for lane-less traffic scenarios due to the irregular and unpredictable nature of traffic density patterns. To overcome these challenges, we propose traffic states to measure congestion patterns in lane-less traffic scenarios. Each traffic state is characterized by the spatio-temporal distribution of neighbouring road users, including vehicles and motorcyclists. We employ traffic graphs to capture the spatial distribution of neighbouring road users. Also, we propose a novel method for the automated construction of traffic graphs by leveraging the detection and tracking of individual road users in aerial videos. Further, in order to incorporate the temporal distribution, we utilize a transformer model to capture the evolution of spatial traffic graphs over time. This enables us to forecast future spatio-temporal distributions and their associated traffic states. Our proposed model, named Traffic State Anticipation Network (TSANet), can effectively forecast future traffic states by analysing sequences of current traffic graphs, thereby enhancing our understanding of evolving traffic patterns in lane-less scenarios. Also, to address the lack of publicly available lane-less traffic datasets, we introduce EyeonTraffic (EoT), a large-scale lane-less traffic dataset containing three hours of aerial videos captured at three busy intersections in Ahmedabad city, India. Experimental results on the EoT dataset demonstrate the efficacy of our proposed TSANet in effectively anticipating traffic states across diverse spatial regions within an intersection. In addition, we also show that TSANet generalizes well for previously unseen intersections, making it suitable for analysing various traffic scenarios without the need for explicit training, thereby enhancing its practical applicability.

Conversions API and Other Signal Resilient Ad Products in a Cookieless World

As the digital marketing landscape evolves towards a cookieless future, advertisers must pivot to new technologies for user tracking and ad personalization. This white paper delves into Conversions API and other signal resilient advertising products that promise to maintain and enhance ad targeting capabilities without infringing on user privacy. We explore the technical underpinnings, industry applications, and best practices for integrating these tools, presenting a comprehensive guide for businesses looking to thrive in a post-cookie world. Through expert insights and actionable strategies, this paper offers a blueprint for advertisers to navigate this paradigm shift and leverage emerging technologies to gain a competitive edge.

Temporal dynamics of user activities: deep learning strategies and mathematical modeling for long-term and short-term profiling

Profiling social media users is an analytical approach to generate an extensive blueprint of user’s personal characteristics, which can be useful for a diverse range of applications, such as targeted marketing and personalized recommendations. Although social user profiling has gained substantial attention in recent years, effectively constructing a collaborative model that could describe long and short-term profiles is still challenging. In this paper, we will discuss the profiling problem from two perspectives; how to mathematically model and track user’s behavior over short and long periods and how to enhance the classification of user’s activities. Using mathematical equations, our model can define periods in which the user's interests abruptly changed. A dataset consisting of 30,000 tweets was built and manually annotated into 10 topic categories. Bi-LSTM and GRU models are applied to classify the user’s activities representing his interests, which then are utilized to create and model the dynamic profile. In addition, the effect of word embedding techniques and pre-trained classification models on the accuracy of the classification process is explored in this research.

Analysis and Improvement of an Authentication Scheme for Fog Computing Services

Fog computing utilizes devices in the edge network to transmit data with very low latency and supports high mobility. However, fog computing inherits security and privacy problems from cloud computing. Therefore, various privacy schemes for fog computing have been proposed to prevent different types of attacks. Recently, Weng et al proposed a fog computing authentication scheme; after analyzing, we found that Weng et al's scheme cannot resist user tracking attack and user impersonation attack. Then, we propose an improved scheme through adding a password, modifying the calculation method of Ei, and adding timestamps. In addition, we also compare the improved scheme with existing authentication schemes in terms of security and computational efficiency. The results show that the improved scheme is more secure and has less computation.

Virtual Fitness Trainer Using AI: A Research Paper

Abstract: Our research delves into the creation of a Virtual Fitness Trainer using Artificial Intelligence (AI) to offer personalized workout guidance and real-time feedback. This project harnesses advanced computer vision, machine learning, and natural language processing techniques to develop an intelligent system capable of accurately detecting human poses, tracking exercise repetitions, and providing corrective feedback. Utilizing Python's OpenCV library to capture live webcam feeds, processed by MediaPipe's BlazePose tool for precise pose estimation, our application employs a novel topology with 33 keypoints, enhancing the accuracy of body movement analysis. The frontend interface, designed with Flask, HTML, CSS, and Bootstrap, ensures an intuitive user experience, allowing selection from various exercises such as squats, curls, jumping jacks, push-ups, lateral raises, and pull-ups. Each exercise page offers detailed instructions and demonstration videos for correct technique execution. The system processes live video streams frame-by-frame, converting them into formats suitable for pose analysis and accuracy assessment. BlazePose tracks user movements in real-time, displaying a 33-keypoint exoskeleton via OpenCV, aiding in understanding body movements and tracking progress through repetition counts. The system also provides real-time feedback on exercise form, suggesting corrective actions to optimize the workout and minimize injury risks. Our Virtual Fitness Trainer addresses the limitations of existing fitness apps by enabling accurate, independent home workouts, bridging the gap left by the absence of gyms and personal trainers. It highlights AI's potential in fitness, with future advancements expected to enhance gesture recognition, adaptive workout plans, and integration with biometric data from wearables. As AI algorithms evolve, the Virtual Fitness Trainer aims to become indispensable for achieving health and wellness goals, offering a convenient, engaging, and supportive platform.

RRefined and Effective Integration of Access Control Mechanism in Blockchain Enabled DApp

The combination of the Internet of Things (IoT) and aviation integration with the help of satellite and 6G communication technologies has led to the Internet of Unmanned Aerial Vehicles (UAV) or Internet of Drones (IoD). To host and share large amounts of drone data in real time, cloud-based IoD algorithm Cloudbased IoD is the wrong choice to reduce the weight of mobile drones. However, how to protect the sensitive drone at the fair is a matter of curiosity, and it is very difficult to break the environment where drone use is available and limited resources are available. Although our previous work in SPNCE21 (PATLDAC) developed an air-on middle of the main load of based drone data access control system with privacy policy, limited access time and user tracking, it provides conflicting and centralized cloud data storage for cloud environment and access with unreliable data. It is not reliable in terms of data access and tracking of users. To this end, we propose a blockchainbased privacy data access control (BPADAC) scheme for distributed and secure drone data sharing on cloudbased IoDs. Building on the granular, traceable, and privacypreserving drone data access capabilities of our previous work, we extend this by using blockchain and decentralized hash tables (DHT) to provide secure and reliable wireless access to humanmachine data and storage. as well as reliable and limited access guarantee mechanisms for cloud drone data sharing services. We also set up a public and denial user tracking system to prevent user abuse and deny the traitor. Finally, we propose a prototype for security analysis and performance evaluation using smart contracts on the Ethereum blockchain to demonstrate the feasibility of BPADAC. hash table, attributebased ciphertext encryption policy, and blockchain-based privacy-protected enabled and aware data access control Key Words: Blockchain, UAV,IoD, Distributed Hash Table, Ciphertext-Policy-Attribute-Based-Encryption, blockchainbased privacy-aware data access control