User Location Information Research Articles

Trajectory privacy preservation model based on LSTM-DCGAN

Rapid scientific and technological development has brought many innovations to electronic devices, which has greatly improved our daily lives. Nowadays, many apps require the permission to access user location information, causing the concern on user privacy and making it an important task to protect user trajectory information. This paper proposes a novel model called LSTM-DCGAN by integrating LSTM (Long Short-Term Memory Network) with DCGAN (Deep Convolution Generative Adversarial Network). LSTM-DCGAN takes the advantages of LSTM to remember attributes in the trajectory data and the generator and the discriminator in DCGAN to generate and discriminate the trajectories. The proposed model is trained using real user trajectory data and the experimental results are validated from the perspectives of both effectiveness and practicality. Results show that the proposed LSTM-DCGAN model outperforms similar methods in generating synthesized trajectories that are similar to real trajectories in terms of the temporal and the spatial characteristics. In addition, various influencing factors are evaluated to investigate ways of further improving and optimizing the model. Overall, the proposed LSTM-DCGAN model can achieve the balance between the effectiveness of privacy protection and the practicality of user trajectory data and can thus be applied to safeguarding user trajectory information.

Improving Data Utility in Privacy-Preserving Location Data Collection via Adaptive Grid Partitioning

The widespread availability of GPS-enabled devices and advances in positioning technologies have significantly facilitated collecting user location data, making it an invaluable asset across various industries. As a result, there is an increasing demand for the collection and sharing of these data. Given the sensitive nature of user location information, considerable efforts have been made to ensure privacy, with differential privacy (DP)-based schemes emerging as the most preferred approach. However, these methods typically represent user locations on uniformly partitioned grids, which often do not accurately reflect the true distribution of users within a space. Therefore, in this paper, we introduce a novel method that adaptively adjusts the grid in real-time during data collection, thereby representing users on these dynamically partitioned grids to enhance the utility of the collected data. Specifically, our method directly captures user distribution during the data collection process, eliminating the need to rely on pre-existing user distribution data. Experimental results with real datasets show that the proposed scheme significantly enhances the utility of the collected location data compared to the existing method.

Pengembangan Aplikasi Peta Interaktif UNJ untuk Mahasiswa dan Pengunjung

The development of information and communication technology has encouraged changes in maps from two-dimensional forms to interactive maps which are more efficient in conveying information. This research discusses the analysis and development of an interactive map of Jakarta State University (UNJ) using the Canva application. With four campus locations spread across Jakarta, Jakarta State University (UNJ) faces difficulties in providing location information for users. Even though they have a web-based information system, none yet displays spatial information on campus buildings. It is hoped that the development of a web-based interactive map will solve existing challenges. Through a Research and Development (R&D) research method approach, with a 4D model. Creating interactive maps using Canva involves design, template selection, adding text, and integration with website links and Google Maps. The research results show the relevance of making UNJ interactive maps in providing more complete and easily accessible information. This development is expected to increase access to information about campuses and the quality of providing geographic information in the digital era.

FLsM: Fuzzy Localization of Image Scenes Based on Large Models

This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.

Location Privacy-Aware Task Offloading in Mobile Edge Computing

In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon ,\delta )$</tex-math></inline-formula> -differential privacy, and the experiments demonstrate the effectiveness of our framework.

Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

Optimizing Secrecy Energy Efficiency in RIS-assisted MISO systems using Deep Reinforcement Learning

This article investigates the maximization of secrecy energy efficiency (SEE) in B5G mobile systems where a suite of reconfigurable intelligent surface (RIS) modules is incorporated. Taking into account the location information of legitimate users and eavesdroppers, we formulate the problem as a joint optimization of the phase shifts, physical orientations, and locations of the RIS modules, as well as resource allocation at the base station (BS). The problem is then solved by leveraging a deep reinforcement learning (DRL) approach proposed in this paper. The case study results demonstrate the effectiveness of the proposed scheme in improving the secrecy energy efficiency of communication systems using RIS.

Restaurant Recommendations Through Natural Language Processing Based on User Rating

This research paper presents a comprehensive study on the development and implementation of a novel Restaurant Recommendation System (RRS) leveraging machine learning techniques and geographical data. The system integrates user preferences, location information, and historical restaurant data to offer personalized recommendations. our research endeavours to contribute to the domain of personalized restaurant recommendation systems. We propose a comprehensive system that amalgamates machine learning techniques, geographical analysis, and a user-friendly graphical interface to offer tailored dining suggestions to users in urban settings. Our approach integrates data pre-processing techniques to handle duplicate entries and encodes categorical variables for enhanced model interpretability. The predictive model, a linear regression algorithm, strives to estimate restaurant prices based on features such as cuisine type, location, and dish category. Geocoding is employed to calculate distances between user-specified locations and recommend establishments within a user-defined radius. The system leverages a Random Forest Classifier to enhance the classification of dish categories, contributing to more precise recommendations. Our system not only predicts restaurant prices but also identifies the most popular establishments based on user-defined parameters. By combining predictive modeling, geographical analysis, and classification algorithms, we aim to create a robust restaurant recommendation system that aligns with the evolving expectations of modern consumers.

Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.

Clustering and Beamwidth Optimization for UAV-Assisted Wireless Communication

With the development of wireless communication technology, unmanned aerial vehicles (UAV) are now widely used in many complex communication scenarios. When a UAV serves as an aerial base station for urban and rural ground users or marine users, it is necessary to consider the clustering of ground users and the energy efficiency of the UAV since the users are usually randomly distributed. For the scenario with randomly distributed ground users and different densities of ground users in urban and rural areas, a clustering and beamwidth optimization method for UAV-assisted wireless communication is proposed. Firstly, the energy efficiency expression of a UAV serving ground users was derived in a downlink wireless communication system assisted by a UAV. Secondly, based on the geographical location information of non-uniformly distributed users, an improved k-means method is proposed to cluster ground users, ensuring that the number of users in each cluster is within an appropriate range. Then, based on the clustering results, a fixed-point iteration (FPI) algorithm was proposed to design the optimal beamwidth of UAVs and improve their energy efficiency. Finally, the superiority of the proposed algorithm in improving energy efficiency was verified through simulation analysis, and the impact of parameters such as the cluster number and transmission power on system energy efficiency was also analyzed.

Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering

Nowadays, there is a large number of web services with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship. However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered. Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services, where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.

LOCATION-BASED AI-ASSISTED FILATION SOFTWARE FOR ISOLATION OF EPIDEMIC DISEASES

Filiation is a technique used to find the first source of the disease in epidemics. As a result of filiation studies, basic and very important information such as the causes of the disease and transmission routes can be obtained. Health units can take preventive measures or plan some health services with the data they have obtained as a result of these studies.&#x0D; &#x0D; The purpose of this study is to follow the spatial movements of people with smartphones in daily life and to isolate the person in case of possible pandemic danger. In this context, it is aimed to determine the locations of infected people in coronavirus and other epidemic diseases, to map the filiation, and to help ensure social isolation. In this context, a tracking software that works on Android and IOS systems with flutter technology has been developed to retrieve user locations. The tracking software transfers the user location information to the central server. When there is a temporal deficiency or possible GPS differences in the received location information, the missing data in the server is estimated by the developed LSTM deep learning model. The model can make accurate predictions over 99%. In the last step of the study, the main tracking and mapping software was developed with C#. In an inquiry made with a positive patient's phone number, positional matches are extracted at the same time as the person. In this way, filiation scanning is performed on the map. As a result, with the widespread use of the study, it is aimed to take faster and more accurate measures to prevent the epidemic from infecting more people. In this context, it is aimed to determine the locations of infected people in coronavirus and other epidemic diseases, to map the filiation, and to help ensure social isolation.

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing.

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users' sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, user location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labeled CSI data. We first focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We then show this data can be used for learning-based localization and enhanced communication research. The presented data set is made fully available to the research community. We illustrate that a CSIbased joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users to improve the spectral efficiency (SE) of the communication system. Finally, we pose promising directions for further exploiting this data set and creating more data sets.

GLDM: Geo-location prediction of twitter users with deep learning methods1

Social networks like Twitter are extremely popular and widely used, which has increased interest in studying the information posted there. One such analytical application is extracting location information of users for real-time monitoring of the objects and events of interest, such as political and social events, disease surveillance, natural calamities, and crime prevention. Identifying geographic location is a nontrivial task, as user profiles contain outdated and inaccurate location information. Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user’s region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets.

A Novel Approach for Location Privacy Preservation of Clusters

Location privacy is one of the burning issues in respect of the LBS and other services, since there are several risks of data leakage happening all over the world as well as the location data leakage is also happening. Withthe increment of these data breaches several advertisements and bad cyber crime happens with different person. To solve these issues and to secure the location privacy as well as location data hiding we have applied k- anonymity algorithm to anonymise the data sets so that the location information of users will hide themselves. The aim of our paper is to demonstrate the novel approach for location privacy preservation using machine learning and deep learning algorithms applicable on various parameters as well as on various data sets. We have applied L diversity and T closeness for the same so that we make sure about the protection of the individual stored data dets as well at the same time.

Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity

With the rapid development of mobile positioning technologies, location-based services (LBSs) have become more widely used. The amount of user location information collected and applied has increased, and if these datasets are directly released, attackers may infer other unknown locations through partial background knowledge in their possession. To solve this problem, a privacy-preserving method for trajectory data publication based on local preferential anonymity (LPA) is proposed. First, the method considers suppression, splitting, and dummy trajectory adding as candidate techniques. Second, a local preferential (LP) function based on the analysis of location loss and anonymity gain is designed to effectively select an anonymity technique for each anonymous operation. Theoretical analysis and experimental results show that the proposed method can effectively protect the privacy of trajectory data and improve the utility of anonymous datasets.

PSafety: Privacy-Preserving Safety Monitoring in Online Ride Hailing Services

Online Ride Hailing (ORH) services gain remarkable development in the past decade, which enable riders and drivers to establish optimized rides via mobile device. To guarantee user safety, ORH service providers often monitor the ride trajectory and report the abnormal behavior once a trajectory deviation occurs. Along with the advantage of safety monitoring raises some vital privacy concerns on user location information leakage. In this paper, we propose a privacy-preserving safety monitoring scheme for ORH services, called pSafety. It enables an ORH service provider to detect user’s trajectory deviation without learning anything about users’ locations. In pSafety, we propose two secure trajectory similarity computation algorithms by using somewhat homomorphic encryption, which are used to plan an agreed path and measure trajectory deviation, respectively. Furthermore, we also design a ciphertext compression algorithm and a secure comparison protocol to improve efficiency. Theoretical analysis and experimental evaluations show that pSafety is secure, accurate and efficient.

Backdoor Federated Learning-Based mmWave Beam Selection

Federated learning (FL) is an emerging paradigm for distributed machine learning that uses the data and the computational power of user devices while maintaining user privacy (e.g., position and motion track). It has been proved a promising way to help the learning-based millimeter wave (mmWave) system achieve efficient link configuration. However, FL systems have an inherent vulnerability to backdoor attacks during training, and this has not received attention in current FL-based beam selection research. The goal of a backdoor attacker is to implant a backdoor in the model such that at test time, the model will mispredict a certain family of inputs, and corrupt the performance of the trained model on specific sub-tasks. We study backdoor attacks in an FL-based beam selection system based on a deep neural network that utilizes user location information. Specifically, we propose a backdoor attack scheme that can be configured in the real world. The attacker’s trigger is an obstacle placed in certain locations. When the model encounters an input with these obstacles, the backdoor will be triggered, and the model will output the beam specified by the attacker. Through experiments, we show that the proposed attack can achieve a high attack success rate in a system without a defense mechanism. Moreover, we show that the traditional norm-clipping defense method cannot effectively defend against our attack. Furthermore, we propose a new backdoor attack defense method and verify the effectiveness of this scheme through experiments. In addition, we propose a backdoor detection method: the federated noise titration method, which can diagnose whether the model has a backdoor. Overall, our work explored backdoor attacks, defenses, and detection of the FL-based mmWave beam selection system.

Privacy-preserving edge computing offloading scheme based on whale optimization algorithm

Aiming at the problem of user’s task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.