User's Personal Information Research Articles

Design of a JSP based Mobile Phone Repair Reservation System

With the development of the times, mobile phones are becoming more and more popular. The increase in the number of mobile phones will lead to an increase in the number of mobile phone repairs. Developing a mobile phone repair appointment system can be beneficial for mobile phone repairs. The mobile phone repair reservation system adopts the B/S mode, using Eclipse software as the system development tool and Java programming language for system development. The system database adopts SQL Server database. In system development, JSP and Servlet technologies are used to help users repair their phones more conveniently, while also benefiting maintenance personnel. The mobile phone repair reservation system designed in this paper consists of a front-end user module and a back-end administrator module. The front-end module implements functions such as registering and logging in for front-end maintenance personnel, modifying personal information, publishing, viewing, modifying, and deleting appointments for maintenance personnel, registering and logging in for front-end users, modifying personal information, making appointments, viewing appointments, and canceling appointments. The backend module enables administrators to modify personal information, view and delete personal information of users and maintenance personnel, view and delete appointment information, and other functions.

A FORENSICS ACTIVITY LOGGER TO EXTRACT USER ACTIVITY FROM MOBILE DEVICES

Mobile devices have become one of the most often used tools in everyday life, mostly because of the importance of its apps. In this case, mobile devices become personal trackers for daily activities that provide important information about the user by recording extra data in addition to the user's personal information. As a consequence of this information gathering, several tools are now accessible for use on mobile devices, however each tool is only able to provide discrete details about a certain application or activity. Consequently, the present research proposes a technology that allows investigators to get a detailed report and time line of all operations performed on the device. This report combines data from several sources to generate a unique collection of facts. Furthermore, an example is provided to illustrate how the solution works, highlighting the practicality of the instrument as well as the way in which investigators need to use it.

Anomaly detection combining bidirectional gated recurrent unit and autoencoder in the context of e-commerce

E-commerce platforms store a large amount of user personal information, transaction data, and financial information, which have extremely high value for hackers and criminals. Therefore, protecting the security of e-commerce platforms is particularly important, and intrusion detection is a technical means used to discover and respond to possible security threats and attacks. But with the development of Internet technology, there are more and more types of intrusion attacks and more sophisticated means. Traditional intrusion detection systems are difficult to cope with. This study proposes an anomaly detection model based on bidirectional gated loop units and autoencoders. The model learns HTTP text data, trains the model, and uses bidirectional gated loop units to convert text sequences from characters to numbers. The experimental results show that when the training set size is 1000, the false alarm rates of Analytic Hierarchy Process, Support Vector Machine, Long Short Term Recurrent Memory Network, and Improved end-to-end algorithm models are 0.30, 0.27, 0.23, and 0.10, respectively. The loss function values are 0.35, 0.28, 0.17, and 0.13, respectively. The F1 values are 0.78, 0.88, 0.91, and 0.99, and the accuracy rates are 0.88, 0.91, 0.95, and 0.99, respectively. The research results indicate that the proposed method model has excellent performance.

Extending limited datasets with GAN-like self-supervision for SMS spam detection

Short Message Service (SMS) spamming is a harmful phishing attack on mobile phones. That is, fraudsters are trying to misuse personal user information, using tricky text messages, sometimes included with a fake URL that asks for this personal information, such as passwords, usernames, etc. In the world of Machine Learning, several approaches have tried to attitudinize this problem, but the lack of available data resources was commonly the main drawback towards a good enough solution. Therefore, in this paper, we suggest a dataset extension technique for small datasets, based on an Out Of Distribution (OOD) metric. Hence, different approaches such as Generative Adversarial Networks (GANs) were suggested, yet GANs are hard to train whenever datasets are limited in terms of sample size. In this paper, we present a GAN-like method that imitates the generator concept of GANs for the purpose of limited datasets extension, using the OOD concept. By using a sophisticated text generation method, we show how to apply it over datasets from the domain of fraud and spam detection in SMS messages, and achieve over 25% relative improvement, compared to two other solutions. In addition, due to the class imbalance in typical spam datasets, our approach is being examined over another dataset, in order to verify that the false alarm rate is low enough.

A Blockchain-Based Privacy Preserving Intellectual Property Authentication Method

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators’ rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations.

Design of Privacy Protection System for Target Search Based on Selection Algorithm and Sustainable Iteration Index

The data self-destruct method based on privacy cloud proposed in this paper can realize the automatic destruction of cloud data after expiration, and based on this method, a self-destruct image protection system is implemented. This project intends to combine the adaptive scale invariant Feature conversion (SIFT) extraction of ciphertext images with binary SIFT to ensure the confidentiality of the images uploaded to the server in the ciphertext space. The security protection of data is realized using Paillier homomorphic encryption technology. The feature of SIFT is extracted from the encrypted region, and it is expressed in binary form to reduce the amount of computation and memory overhead. The experimental results show that this method can effectively prevent the automatic destruction of images after expiration, so as to ensure that the user's personal information is not leaked.

Enhancing User Privacy Through Ephemeral Sharing Design: Experimental Evidence from Online Dating

In the dynamic world of online dating, a key challenge faced by platforms is the cold-start problem, where newly matched users are hesitant to engage due to privacy concerns. Our solution, ephemeral sharing, addresses this by balancing privacy with the need for personal information sharing. This feature allows personal photos to disappear and become untraceable soon after being viewed, reassuring users about their privacy. We conducted a large-scale randomized experiment with more than 70,000 users to evaluate the impact of ephemeral sharing. The results are compelling: users who could share ephemeral photos were more likely to send personal images alongside with their matching request, especially those with human faces, leading to more matches and higher engagement. Significantly, this effect was more pronounced among users who are more sensitive to their privacy. Furthermore, ephemeral sharing was found to reduce users’ concerns related to data collection, dissemination, and identity misuse, thereby increasing the willingness to share personal information. This approach not only enhances user privacy but also stimulates more active engagement on the platform. For dating platforms and similar platforms, adopting ephemeral sharing can revolutionize user experience. It provides a strategic advantage by boosting user personal information sharing and enhancing privacy, crucial for maintaining meaningful communication in online dating. This feature represents a significant step forward in designing user-centric, privacy-conscious platforms.

Cost-effective internet of things privacy-aware data storage and real-time analysis

<span lang="EN-US">It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.</span>

Protection of Personal Information of E-Commerce Platform Users

Protection of Personal Information of E-Commerce Platform Users

The potential of Blockchain in improving the security of Websites

With digital transformation and the rise of cyber threats, website security has become a vital part of the Internet. Websites contain a large amount of sensitive information, such as user personal data, financial information and trade secrets. Criminals are constantly looking for vulnerabilities to attack and steal data, which can lead to serious consequences for users and website owners. This study provides an extensive analysis and comparative study of the effectiveness of blockchain technology compared to traditional website security methods. The main focus of the study is on evaluating key parameters such as decision accuracy, query processing speed, and overall system reliability. During the study, the influence of the number of blocks in the blockchain chain and the quality of encryption on the accuracy of decision-making was revealed. The ability of the system to effectively contain potential threats was analyzed. The speed of processing requests was evaluated taking into account the possible impact of increasing the number of blocks and the complexity of encryption on system performance. A comprehensive formula for assessing the overall reliability of the system has been developed, which combines the positive and negative aspects of blockchain technologies. The obtained results indicate that by integrating factors of accuracy and speed of processing into a comprehensive assessment of system reliability, the use of blockchain allows to increase the overall reliability of the website protection system.

FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks

The recommendation system can alleviate the problem of ?information overload?, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preferences under user behavior sequences, the global preference modeling of users is often underestimated, and usually, only some simple and crude user latent representations are introduced. Therefore, this paper proposes a sequential recommendation based on Fusing Session-Aware models and Self-Attention networks (FSASA). Specifically, we use the Self-Attentive Sequential Recommendation (SASRec) model as a global representation learning module to capture long-term preferences under user behavior sequences and further propose an improved session-aware sequential recommendation model as a local learning representation module from user model the user?s dynamic preferences in the historical behavior, and finally use the Gated Recurrent Unit (GRU) module to calculate their weights. Experiments on three widely used recommendation datasets show that FSASA outperforms state-of-the-art baselines on two commonly used metrics.

Data Privacy and Technological Ethics in Rural Area

In today's world of rapid increasing of technology, Data privacy and Ethics has become a major concern. The complexity of data and ethics poses incredible issues by collecting user personal information in digital world. the Digital service company usually bear responsibility of protecting users' personal data from unauthorized access however issue arise when service provider and platforms do not protect user collective data, and this led to data misuse and exposing sensible data of the person. To handling once data responsibly and ethically is important for safeguarding people's rights and preserving public trust. This study examines the ethical challenges in emerging technologies, including data privacy and ethics. The paper aims to provide awareness of data privacy and ethics in technology.

Influences of displaying permission-related information on web single sign-on login decisions

Web users are increasingly presented with multiple login options, including password-based login and common web single sign-on (SSO) login options such as “Login with Google” and “Login with Facebook”. There has been little focus in previous studies on how users choose from a list of login options and how to better inform users about privacy issues in web SSO systems. In this paper, we conducted a 200-participant study to understand factors that influence participants' login decisions, and how they are affected by displaying permission differences across login options; permissions in SSO result in release of user personal information to third-party web sites through SSO identity providers. We compare and report on login decisions made by participants before and after viewing permission-related information, examine self-reported responses for reasons related to their login decisions, and report on the factors that motivated their choices. We find that usability preferences and inertia (habituation) were among the dominant factors influencing login decisions. After participants viewed permission-related information, many prioritised privacy over other factors, changing their login decisions to more privacy-friendly alternatives. Displaying permission-related information also influenced some participants to make tradeoffs between privacy and usability preferences.

An IoT measurement system for a tailored monitoring of CO2 and total volatile organic compounds inside face masks

This paper proposes an innovative IoT-based system for monitoring air quality inside protective equipment such as FFP2 masks, which have become more widespread due to the COVID-19 pandemic. The system aims to provide diagnostic elements to specialist doctors and suggest a healthier use of the mask by monitoring the concentration of pollutants. The system aggregates data over a 15-minute window and calculates average values for each measured parameter, comparing them with reference thresholds to suggest removing the mask if necessary. An innovative aspect is personalized monitoring of exhaled breath, specifically Volatile Organic Compounds (VOCs), providing a customized and reliable information framework for doctors. This is possible thanks to the integration of removable memories, inside which the user's personal information and metrological characteristics of the system are stored in a standardized form. The proposed platform is accessible to both users and doctors, enabling early diagnosis by providing a complete picture of the patient's specific condition. This solution can have a strong impact on daily life and well-being, especially for diseases that increase the presence of certain compounds in the exhaled breath.

Research on Prediction of User's Entrepreneurial Learning Behavior based on Neural Network

This study aims to explore the predictive capability of neural networks in user entrepreneurial learning behavior. Traditional methods have limitations in predicting user entrepreneurial learning behavior. Therefore, neural networks are adopted as predictive models, analyzing multidimensional data such as user personal information, learning history, and learning behavior. The research methodology includes data collection and preprocessing, feature engineering, design and training of neural network models, as well as evaluation and interpretation of prediction results. Through experimentation and result analysis, the neural network models demonstrate good accuracy and predictive ability in forecasting user entrepreneurial learning behavior. The research results further explain the key factors influencing user entrepreneurial learning behavior and provide insights for improving entrepreneurial education and learning platforms. This study offers new methods and theoretical foundations for predicting user entrepreneurial learning behavior, significantly contributing to enhancing learning outcomes and entrepreneurial success rates for entrepreneurs.

EVALUATING THE QUALITY OF INFOLOKER KAB.KARAWANG WEBSITE USING WEBQUAL 4.0 METHOD

Technology has provided new methods of presenting the information. Information can be easily accessed anywhere and anytime. One of the Regional Governments of Indonesia takes this advantage by creating an information system called InfoLoker Kab. Karawang to make it easier for citizens to find job vacancies. Of this importance, it interests researchers to conduct a study to evaluate the website’s quality based on the dimensions of the WebQual 4.0 method. WebQual is a method for measuring a website's quality based on the end user's point of view. The data source is taken from InfoLoker Kab. Karawang website users through a survey method with a sample of 118 respondents and data analysis using SmartPLS. According to the discussion results, the quality of the InfoLoker Kab. Karawang’s website was good, with ratings of 73.3%, 74.3%, and 62.2% for the dimensions of usability, information quality, and service interaction quality, accordingly. Overall user satisfaction is significantly impacted by the quality of the information and service interactions, but not by usability because usability’s t-value is under 0.96. However, the website's quality can still be improved, particularly in the service interaction dimension, by improving the security of the user's personal information because even though the service interaction quality dimension significantly influences user satisfaction, it has the lowest quality score

E-Brightpass: A Secure way to access social networks on smartphones

Social network providers offer a variety of entertainment services in exchange for end users’ personal information, such as their identity. The majority of users access social networking sites via their smartphones, which they utilize in conjunction with a traditional authenticator like a password. On the other hand, aggregators, which pull content from multiple social networks, are often used to get into smartphone apps that may involve mobile ticketing, identification, and access control. They are a potential target for malware and spyware injections due to their powerful position. Malware is capable of circumventing authentication mechanisms in order to get access to social networking services, which may result in stealing the personal information of users. To deflect any type of attack from malicious software, BrightPass [22], a malware-resistant method based on screen brightness, was introduced. Conversely, we have demonstrated that the BrightPass user’s personally identifiable information, such as PIN numbers, may be recovered by evaluating the variations between the recorded input from many authentication sessions. We have then offered various enhanced BrightPass versions to address the observed vulnerability. Our enhanced BrightPass versions are both simple and secure to use when it comes to accessing social networks via mobiles.

Research on Blockchain Anonymous Communication Based on Key Derivation

Abstract With the continuous development of the Internet and communication technologies, network communication provides convenience but also brings security problems such as exposure of users' personal privacy information and theft, private tampering, and forgery of false information. Modern cryptography technology is an important safeguard against message eavesdropping and tampering, while the rapidly developing anonymous communication technology in this century makes it difficult for attackers to infer user's personal information and communication relationships. In response to the potential threats of traditional centralized systems such as central nodes being vulnerable to attacks and data storage being tampered with, this paper proposes a blockchain anonymous communication algorithm KDAC based on key derivation, which takes advantage of the decentralization, data immutability, consensus mechanism and anonymity of blockchain and combines the ECC cryptographic derivation algorithm and anonymous communication technology to realize the key-at-a-time, one-address-at-a-time The key derivation scheme ensures the message integrity and tamper-evident while effectively hiding the identity information of both communication parties. In addition, this paper also optimizes the blockchain anonymous communication system with key derivation. Users only need the initial key of blockchain nodes to join the network for communication, and the information transmission is difficult to trace based on the blockchain network, which can effectively guarantee communication security and anonymity. The experimental results show that the efficiency of the derived key algorithm is roughly in the same order of magnitude as that of the 256-bit AES symmetric encryption algorithm, which can play a better role in practical applications. On the other hand, the derived key generated based on the algorithm has complete randomness in association verification, and it is impossible to reverse the initial parameters, which can well guarantee the anonymity of user identity.

To Analyse Human Sentiments on Social Media Platforms and Their Interest Inference by Using Deep Learning

Abstract: In the modern environment, human emotions are complicated and multifaceted. People are exposed to a wide range of information, ideas, and experiences as a result of the quick development of technology and globalisation, which can affect how they feel and how they view the world. from multiple social network platforms that users engaged in. User interest exhibits dualheterogeneities: it is complementarily and multiple social networks reflects them comprehensively; interest of user are not independent of each other rather inter-correlated in a nonuniform way. Although past approaches towards these problems had great success that considers the dual heterogeneities simultaneously, consistency of sources, uses multi-sourced and multi-tasked learning scheme. To address this, a web application coupled with machine learning technique like linear regression to predict the human sentiments by inviting them to attempt the survey. The answers to the survey are per-processed to identify the relationship between different independent variables and a dependent variable that represents a particular sentiment. A database utilising MYSql is used to record the user personal information, the responses, and the quiz results of users who have registered while unknown users are first requested to sign up. Pre-trained data and Supervised Learning algorithms can be used to detect sentiments.

Smile, YOU'RE Being Watched: Digital Privacy and Risk Perception Regarding Data Sharing on Social Networks

Abstract This study analyzes how users perceive the risks associated with privacy loss on social networks and behave in response to these perceived risks. Specifically, it studies how privacy literacy influences privacy-related risk perceptions concerning the usage of personal information on social networks, and the relationship between the user's privacy concerns and their sharing behavior on social media. The privacy paradox concept is evaluated in this context with respect to the ambiguous values and perceptions that social network users have, wherein they experience concerns regarding their privacy in specific environments but continue to share their information and personal content via these channels regardless. The research adopts a qualitative exploratory approach using semi-structured interviews and content analysis, with Facebook as the focal social network. Four categorical headlines are identified, supporting specific findings that indicate that risk perceptions concerning privacy do impact users' privacy concerns, but do not influence individuals to reduce sharing data. Thus, although some users perceive risks involved in these practices, they continue to share data, ignoring the fact that this may be to their detriment and instead focusing on characteristics that support their sharing. The findings indicate that social interaction benefits obtained from social, hedonic and utilitarian elements provided via the social network lead users to take risks regarding online self-exposure based on their desires and social values.