Protect User Data Research Articles

Camguard: Preventing unauthorised camera access for women’s saftey

Online harassment is a serious problem that can have significant negative impacts on the mental health and well-being of its victims. To address this issue, an Android app has been developed that is accessible to a wide range of users worldwide. The app is built on a script backend process, providing a stable and efficient environment for data management and request processing. The app's API is integrated into the script backend process, ensuring seamless and efficient operation.During the installation process, the app requires users to go through a verification process that includes providing their Google Account, mobile number, and an OTP. This ensures that only legitimate users have access to the app's features and functions. The app also requires users to create a strong master password, which is directly connected to the Google bot, enhancing its security. Biometrics are recommended to protect the master password, ensuring that only authorized users can access their data.The app's security measures are crucial in ensuring the privacy and protection of user data. The use of biometrics and direct connection to the Google bot enhances security, and the app's API provides access to relevant information and enhances the user experience. Users can change their password securely, ensuring that only authorized users can access their data.Overall, the app is a reliable and efficient solution for managing and protecting user data. Its compatibility with a wide range of devices and seamless integration of API make it accessible to a large percentage of Android users worldwide. The app's verification process, use of biometrics, and direct connection to the Google bot enhance its security, ensuring that only authorized users have access to the app's functionalities. The development of this app is a critical step in addressing the issue of online harassment and protecting the privacy and well-being of its users.

Front-end security architecture: protection of user data and privacy

Front-end security architecture: protection of user data and privacy

Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

<abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

Faster abnormality localization and recognition in a secured video bitstream by implementation of video encryption techniques

Cloud video storage uses an encrypted format to protect user data. It means encrypted video processing is an essential part of secured cloud storage. In order to detect suspicious or anomalous behavior, video surveillance must have encrypted cloud access. The primary goals of this research are to estimate parameters and detect abnormalities in an encrypted video bitstream. Various typical properties of video encoding frameworks and format-compliant encryption algorithms are investigated to identify abnormalities in an encrypted video bitstream using format-compliant encryption. The encrypted bitstream is decrypted to get three different kinds of enhancement features: the sizes of macroblocks, partitions of macroblocks, and the magnitude of the motion vector. The identification and localization methods now do not include video decryption or complete decompression. The proposed strategy has been created to implement the video encryption scheme efficiently and is compatible with various video encryption techniques. The experimental findings demonstrate that, in comparison to other methods, the proposed approach provides optimal running time and detection rate performance.

Legal regulation of digital platforms for recreational physical education in the Kyrgyz Republic

The article focuses on the legal regulation of digital platforms designed for health and fitness activities in the Kyrgyz Republic. With the rapid development of technology and the increasing number of digital service users, there is a need to adapt legislation to new conditions. The article analyzes current legal acts regulating the use of digital platforms in the field of physical culture, identifies existing problems, and suggests ways to solve them. Special attention is paid to issues of user data protection, ensuring the quality of provided services, and legal mechanisms for controlling the activities of digital platforms. The results of the study can be useful for the development and improvement of legislation in this area, as well as for practical application in the work of government agencies and commercial organizations.

The Use of Artificial Intelligence in Human Resources Processes as Part of Sustainable Development: Political and Organizational Aspects

The article characterizes and defines the directions of artificial intelligence technology use in modern organizations and discusses those categories that will remain promising in the future. It highlights the main advantages and risks that currently exist with respect to the use of artificial intelligence and its development in Human Resources processes (HR processes). It was concluded that the main areas of use of artificial intelligence technology in modern organizations and categories that will continue to be promising in the future are defined: automation and optimization of processes; generating insights for decision making. It was emphasized that, in order to prevent the emergence of threats to humanity, in the process of developing artificial intelligence, specialists must establish certain restrictions and its developers must prioritize the issue of protection of user data and ensure control of its use.

Designing End-to-End Web-Based Application Encryption with Asymmetric Encryption Using Waterfall Methodology

An in-depth exploration of a robust and systematic approach aimed at enhancing the security and integrity of communication systems within a website is conducted in this research. The focus is strengthening the interaction between the server and the user or client. Thus, secure data transmission can be guaranteed. This research integrates the well-established and widely respected waterfall methodology with asymmetric encryption techniques, explicitly using the RSA algorithm, into the overall development process. This method covers data encryption and decryption comprehensively. Blackbox testing validated the results for the application's expectation of increased research reliability. Advanced security measures are integrated into projects that use these insights to protect user data due to fast-growing cyber threats and the importance of data privacy.

YouTube’s Evolution, Marketing Strategies, and Impact on Digital Media Landscape: A Comprehensive Analysis

YouTube, the pioneering video social media platform, boasts a staggering 2.2 billion monthly active users, ranking as the second most-used platform globally. With 81% of U.S. adults using YouTube, its potential is undeniable. This article provides a comprehensive analysis of the rise of YouTube, its marketing strategies, and its impact on the digital media landscape. YouTube’s success stems from its openness, innovativeness, and cooperation with Google, especially in video advertising, making it a major channel for digital advertising. The article also explores the challenges YouTube faces, such as threats from competitors and user privacy and data protection issues. Finally, the article offers recommendations to address these challenges and further strengthen YouTube’s leadership in digital media. YouTube’s impact on digital marketing is undeniably cementing its status as a leading video-sharing platform and cultural phenomenon. The article aims to provide valuable insights and inspiration for digital media practitioners and researchers by delving into YouTube’s success.

Online Platform Privacy Policies

In January 2021, Meta (then Facebook) published an update to its WhatsApp privacy policy which included, among other things, a decision to share some user data with Facebook. This action, together with previous events like the notorious Cambridge-Analytica data breach, has sparked debates about the protection of online privacy, specifically the balance of rights and responsibilities relevant to the protection of social media user data. This qualitative study contributes to that debate by exploring the perceptions and reactions of South African WhatsApp users to these policy changes. The findings highlight the significance of platforms like WhatsApp in the daily socio-economic activities of users in developing countries. This reliance on social media for communication and access to vital information creates an imbalance of power between users and platform owners. The situationis worsened by the lack of effective regulatory frameworks that governments and institutions in developing countries can use to enforce their privacy laws, leaving users vulnerable to potential exploitation from digital platforms. This study contributes to the broader discourse on safeguarding online privacy as Western technology companies continue to gain access to data that is generated by an ever-increasing global user base.

Unified System for Processing Non-Personalized Data

The purpose of the study. The purpose of this article is to study the possibility of creating a unified register of non-personalized data, that is, data that cannot be attributed to a specific subject of personal data. The objective of the study is to analyze relevant materials for the purpose of the study, to determine the need for this system, to determine possible areas of application of the system, to determine the system sources and data on which the system can be based. The object of the study is a unified system for processing non-personalized data, the issue of creating this system and the opportunities that open up to the user.The subject of the study is the question of the data that can be processed in this system, as well as the section in which the data can be interpreted.Research methods. The article uses the following research methods: analysis of open data sources, modeling of the target solution in the form of a concept.Results. The article presents an example of the implementation of a system for processing non-personalized data. The variants of systems that currently have a set of data on personal data subjects and are ready to conduct a preliminary scoring analysis are presented. A possible result of the application of scoring is presented, using the example of the banking industry. An analysis of the current state of citizens' data processing systems was carried out. The results of the analysis showed that currently there is no single system that would allow for scoring analysis on the subject of personal data, aggregating data from various sources of state systems. There are no data in open sources on the progress of the development of a system that allows for scoring analysis based on non-personalized data. At the same time, the creation of a unified system for processing non-personalized data can both have a positive impact on the economic indexes of consumers connected to it, and contribute to the implementation of strategic plans of the state.Conclusion. During the study, a project was proposed for implementation to create a unified system for processing non-personalized data. A variant of data that can be used for primary scoring is proposed. The advantages of this system include a positive economic effect for organizations connected to the system, due to the reduction of monetary losses, improving the quality of sales, increasing customer loyalty, for the state, a positive effect can be an increase in the transparency of citizens' data and a decrease in the number of economic crimes, for citizens, the positive side of the system is to reduce information noise, due to a decrease in the number of studies from the companies, reduction of fraudulent actions, in relation to consumers. The disadvantages of the system include a high risk of data leakage, as a result of an increase in the number of crimes, incorrect interpretation of data by the scoring model, unavailability of various services for clients of organizations due to lack of data, the risk of data misuse by the operator of personal data processing. As a result, we can conclude that with the proper level of user data protection and clear rules for working with data, this system has good prospects for achieving the economic goals of both private companies and the state.

A Differential Privacy protection-based federated deep learning framework to fog-embedded architectures

Nowadays, companies collect massive quantities of data to enhance their operations, often at the expense of sharing user sensible information. This data is widely used to train Deep Learning (DL) neural networks to model, classify, or recognize complex data. These activities enable companies to offer an array of services to users, such as precise advertising and optimal location services. This study explores potential solutions for preserving privacy while utilizing DL applications.To address the privacy issue, we develop a privacy-preserving framework specifically designed for fog computing environments. Unlike traditional cloud computing architectures, fog embedded architectures only share a small portion of user data with a nearby fog node, ensuring that the majority of sensitive data remains secure. Within these fog nodes, we incorporate two additional algorithms, namely Generalization and Threshold, to enhance the privacy-preserving capabilities of the framework.The first algorithm, Generalization, introduces a validation dataset within the fog nodes which not only increases the accuracy of the fog-embedded framework but also ensures that user data is preserved. The second algorithm, Threshold, is responsible for protecting user data samples and reducing the amount of information sent to the server. By combining these two algorithms, we are able to provide an additional layer of protection for user privacy while still maintaining the accuracy of the model.We conduct an evaluation to test its effectiveness using two separate datasets. In addition, we analyze them through a Feed Forward Neural Network (FFNN) and compare the results with a traditional centralized architecture to validate the effectiveness of the proposed framework.The results of our evaluation demonstrate that the proposed privacy-preserving framework, when combined with the Generalization and Threshold algorithms, can preserve up to 38.44% of user data. Additionally, we were able to extend the framework to multiple fog nodes without compromising the network’s accuracy, as we only observed a 0.1% decrease in accuracy when using the proposed architecture.This study emphasizes the importance of preserving user information while using DL applications and provides a solution that trains the desired network without violating user privacy, hence preserving their anonymity. Overall, the study highlights the potential of Federated Deep Learning to improve the accuracy and privacy of DL applications in fog computing environments.

IMPLEMENTATION OF BCRYPT ALGORITHM ON WEBSITE-BASED HASHING GENERATOR USING LARAVEL FRAMEWORK

Implementation of the Bcrypt algorithm on a website-based hashing generator using the Laravel framework has been carried out in this study. The background of this research is based on the development of information technology which has affected many aspects of human life, where data and information are very important and need to be protected. Web-based information systems are vulnerable to security threats, such as wiretapping and intrusion by unauthorized parties. Therefore, the implementation of information security aims to overcome these problems and provide solutions both technically and non-technically. This research uses the Laravel framework, an open-source PHP framework that enables the development of web applications with modules that optimize PHP performance. Bcrypt and salt algorithms are used in the hashing process to protect user data. The results of the research show that using Laravel as the right framework for making hashing generators. The result of the hashing process produces a constant value of 60 characters, even if the input text is more than 60 characters with random text. The cost factor, which is a factor that regulates hashing power, affects the time required for the hashing process and hash verification. Using the Bcrypt generator tool is useful in cross-browser testing, especially in tests involving hashed passwords. With this tool, many valid bcrypt password hashes can be generated for testing. This research makes an important contribution in improving the security of web-based information systems by applying the Bcrypt algorithm to the hashing process. This implementation can provide better data protection for users and improve overall system performance.

Phishing Website Detection using Machine Learning: A Comprehensive Study

Phishing attacks continue to be a serious danger to consumer privacy and internet safety. Attackers use false websites that look like authentic ones to steal sensitive data, including login credentials and financial information. In the study, different machine learning techniques for categorizing phishing websites are evaluated, including decision trees,logistic regression,Multinomial NB.This study also explores the potential integration of the generated models into online browsers and security systems, which would offer real-time defense against phishing assaults.The results of this study help to improve internet security, protect user data, and lessen the effect of phishing assaults on the digital world.

Unveiling the Dark Side of Fintech: Challenges and Breaches in Protecting User Data in Indonesia’s Online Loan Services

The rapid evolution of information and communication technology has driven diverse business and financial practices. Indonesia is at the forefront with high engagement in fintech online lending services, presenting challenges in safeguarding user data despite these platforms' convenience. This research aims to analyze the intricacies of protecting the personal data of users of online lending services in Indonesia and highlight the obstacles faced in this process. Using a normative legal approach combined with descriptive data analysis, this research examines the protection mechanism from the perspectives of users and online loan service providers. The research concluded that users should verify the legitimacy of online loan service providers by ensuring proper registration with the Financial Services Authority (OJK) of the Republic of Indonesia. In addition, users should carefully scrutinize and understand the terms and conditions of personal data protection before agreeing to an online loan agreement. Second, the main problems qualified as obstacles in this research related to the effectiveness of personal data protection in the context of online lending are at least influenced by three main elements that influence each other, especially those related to legal substance, legal structure, and legal culture. Based on this doctrine, it is found that in addition to legal uncertainty related to the guarantee of personal data protection rights on the one hand, on the other hand, the absence of specialized institutions related to personal data protection is one of the factors that affect the ineffectiveness of personal data protection in Indonesia related to online loans.

Cybersecurity Challenges and Solutions in the Fintech Mobile App Ecosystem

The rapid growth of the fintech industry, driven by the proliferation of mobile applications, has revolutionized financial services, providing unprecedented convenience to users. However, this innovation comes with inherent cybersecurity challenges that demand rigorous attention. This study delves into the complex and ever-evolving landscape of cybersecurity within the fintech mobile app ecosystem, aiming to identify challenges and present viable solutions. Cybersecurity threats in the fintech mobile app ecosystem encompass a broad spectrum, including data breaches, malware attacks, phishing schemes, and identity theft. As fintech apps handle sensitive financial data and transactions, they are prime targets for malicious actors seeking financial gain. To address these threats, this research examines current cybersecurity strategies and emerging technologies, such as advanced encryption, biometric authentication, and AI-driven anomaly detection. Furthermore, regulatory frameworks and industry standards play a crucial role in shaping cybersecurity practices within fintech. This study assesses the impact of compliance requirements on fintech companies and their ability to protect user data. Real-world case studies and incident analyses provide valuable insights into the consequences of cybersecurity breaches in this sector. Ultimately, this research aims to contribute to a comprehensive understanding of the multifaceted cybersecurity challenges faced by the fintech mobile app ecosystem and offers practical recommendations for fintech firms, regulators, and cybersecurity professionals to enhance security measures. Strengthening the security foundation is paramount to sustaining user trust, fostering continued innovation, and securing the future of mobile fintech.

HN3S: A Federated AutoEncoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing

Federated recommender systems can serve users with suitable item recommendations while preserving their privacy, but most current works cannot serve non-participant users. We consider that Federated AutoEncoder for Collaborative Filtering (FAE-CF) is expected to solve this problem. However, FAE-CF encounters privacy protection concerns and high communication overhead issues. In this paper, we investigate FAE-CF’s privacy protection capabilities and communication volume, unveiling its potential privacy security issues and inferior practicality. By analyzing the FAE-CF gradient, we propose an effective attack that could recover most user data, with all recovery metrics above 95.53% in our simulation experiments. To address these challenges, we propose Hybrid Negative Sampling and Secret Sharing (HN3S), a privacy-preserving method designed for FAE-CF. HN3S uses hybrid negative sampling to reduce communication overhead and secret sharing to protect user data. Experiments conducted on various public datasets with multiple AE-CFs have shown that HN3S can effectively protect the privacy of FAE-CF, reducing the communication overhead by up to 60.44%, while maintaining the performance reduction within 10.78%. Our research reveals significant privacy risks and poor utility inherent in current FAE-CFs, and provides a feasible solution to protect user privacy and balance communication overhead with algorithm performance.

Privacy-preserving recommendation system based on user classification

Recommender systems have become ubiquitous in many application domains such as e-commerce and entertainment to recommend items that are interesting to the users. Collaborative Filtering is one of the most widely known techniques for implementing a recommender system, it models user–item interactions using data such as ratings to predict user preferences, which could potentially violate user privacy and expose sensitive data. Although there exist solutions for protecting user data in recommender systems, such as utilising cryptography, they are less practical due to computational overhead. In this paper, we propose RSUC, a privacy-preserving Recommender System based on User Classification. RSUC incorporates homomorphic encryption for better data confidentiality. To mitigate performance issues, RSUC classifies similar users in groups and computes the recommendation in a group while retaining privacy and accuracy. Furthermore, an optimised approach is applied to RSUC to further reduce communication and computational costs using data packing. Security analysis indicates that RSUC is secure under the semi-honest adversary model. Experimental results show that RSUC achieves 4× performance improvement over the standard approach and offers 54× better overall performance over the existing solution.

A secure and effective data aggregation in WSN for improved security and data privacy

The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.

Privacy and Efficiency of Communications in Federated Split Learning

Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors.Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user data and privacy better while ensuring high performance. Both of these distributed learning architectures have advantages and disadvantages. In this paper, we examine these tradeoffs and suggest a new hybrid Federated Split Learning architecture that combines the efficiency and privacy benefits of both. Our evaluation demonstrates how our hybrid Federated Split Learning approach can lower the amount of processing power required by each client running a distributed learning system, reduce training and inference time while keeping a similar accuracy. We also discuss the resiliency of our approach to deep learning privacy inference attacks and compare our solution to other recently proposed benchmarks.

Privacy and Security Challenges in Online social media: A Case Study Analysis

The rapid progress of online social media platforms has fundamentally reshaped how persons connect, communicate, and share information. While these platforms offer incomparable opportunities for worldwide interaction, their exponential development has introduced complex challenges concerning security and privacy. This research investigates into the complex landscape of online social media, addressing the supreme significance of safeguarding user privacy and enhancing security measures. By examining key concepts, theories, and case studies, this study puts light on the subtle balance between users' expectations of privacy and the susceptibilities essential in data collection, profiling, and cyber threats. The research examines -pivotal case studies, such as the Facebook-Cambridge Analytica scandal (2018) and the Twitter hack of 2020, to illustrate the deep implications of privacy breaches and security compromises. Through the synthesis of insights from theoretical frameworks and regulatory responses, this study presents a complete perspective on the relationship between privacy and security, underscoring the need of informed user consent, transparent data governance, and strong security mechanisms. The findings contribute to an enhanced understanding of the complex dynamics within online social media platforms, providing actionable recommendations for cultivating a safer and safer digital environment. As technology continues to advance, this research offers valuable insights for navigating the evolving challenges of the digital age while upholding fundamental principles of user privacy and data protection.