SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted The Impact of Privacy Awareness on Sharenting and Privacy Management Practices Among Saudi Parents(Kent State University, 2025) alnemre, Afnan; Hollenbaugh, Erin; Egbert, NicholeIn the digital age, parents frequently engage in the practice of sharenting—sharing their children's photos and information on social media—raising concerns about privacy and its implications for identity theft and other risks. This phenomenon has significant impacts on the violation of children’s privacy and their psychological and social well-being. Guided by communication privacy management (CPM) theory, this study aimed to identify the role of privacy awareness and religiosity in sharenting and privacy management practices among Saudi parents, using a quantitative approach and relying on a survey as a data collection method, N =139 were collected from Saudi parents. The findings revealed that extrinsic religiosity demonstrated positive relationship with all sharenting dimensions self-control, social behaviors and implications also with boundary linkages and boundary permeability in CPM. Also, intrinsic religiosity has a negative correlation with boundary permeability, while the study does not find a relationship between intrinsic religiosity and all sharenting dimensions. Additionally, there was a significant negative correlation between privacy awareness and boundary ownership in CPM. Moreover, the study has shown gender differences in sharenting behavior, showing that mothers post more pictures of children. This research aspires to inform initiatives promoting privacy-conscious digital practices among parents and aims to support the development of child protection policies in Saudi Arabia to safeguard children's online privacy.25 0Item Restricted MEASURING AWARENESS AND ABILITY OF STUDENTS IN SECURING PERSONAL SENSITIVE DATA ON MOBILE PHONES(University of North Texas, 2024-12) Bukhari, Ahmed Abdulhakim; Allen, JeffThis study investigates the awareness and ability of students at the University of North Texas (UNT) in securing their personal and sensitive information on mobile phones. In an era marked by increasing digitization, mobile phones play a pivotal role in our daily lives, making it essential to understand the practices and knowledge of individuals when it comes to safeguarding their personal information. To achieve this, the study adopts a multidimensional approach through the integration of three prominent theoretical frameworks, which are the technology acceptance model (TAM), the theory of planned behavior (TPB), and protection motivation theory (PMT). This integrated framework enables a comprehensive understanding of student perceptions, intentions, and motivations concerning mobile phone security. To gather data, a quantitative research method was employed, using a structured survey in the form of a questionnaire. Respondents were asked to rate their agreement with various statements using a 5point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. The survey included questions designed to assess student awareness, knowledge, attitudes, and behaviors related to securing personal information on their mobile phones. The findings of this study shed light on the existing gaps in the knowledge and practices of students related to mobile phone security. The outcomes can inform educational institutions and policymakers on the necessity of implementing awareness programs and security measures to protect personal information in the digital age. This research contributes to a deeper understanding of mobile phone security practices and paves the way for potential interventions to empower UNT students and users of mobile technology to protect their sensitive data effectively.52 0Item Restricted Leveraging Blockchain for Trust Enhancement in Decentralized Marketplaces: A Reputation System Perspective(Old Dominion University, 2024-07) Aljohani, Meshari; Olariu, Stephan; Mukkamala, RaviCentralized marketplaces provide reliable reputation services through a central authority, but this raises concerns about single points of failure, user privacy, and data security. Decentralized marketplaces have emerged to address these issues by enhancing user privacy and transparency and eliminating single points of failure. However, decentralized marketplaces face the challenge of maintaining user trust without a centralized authority. Current blockchain-based marketplaces rely on subjective buyer feedback. Additionally, the transparency in these systems can deter honest reviews due to fear of seller retaliation. To address these issues, we propose a trust and reputation system using blockchain and smart contracts. Our system replaces unreliable buyer feedback with objective transaction assessments. Performance challenges of blockchain-based systems are tackled through three innovative schemes, resulting in a substantial improvement over the baseline approach. Furthermore, we proposed a decentralized marketplace utilizing blockchain-based smart contracts to address privacy concerns in buyer reviews that arise from the transparency of decentralized marketplaces. This enables buyers to use one-time identities for reviews to promote anonymity. This system ensures that buyers provide reviews by requiring a review fee, which is fully refunded after the review is submitted. Moreover, we proposed a trust and reputation service based on Laplace’s Law of Succession, where trust in a seller is defined as the subjective probability that they will fulfill their contractual obligations in the next transaction. This method accommodates multi-segment marketplaces and time-varying seller performance, predicts trust and reputation far into the future, and discounts older reputation scores. In addition, we propose SmartReview, an automated review system utilizing blockchain smart contracts to generate objective, bias-free reviews. The review module is designed as a smart contract that takes the contract terms and the evidence provided by the buyer and seller as inputs. It employs advanced computer vision and machine learning techniques to produce quantitative and qualitative reviews for each transaction, ensuring objectivity and eliminating reviewer bias. Lastly, we introduce a structured blockchain architecture featuring a layered approach. This architecture includes mechanisms for secure transaction recording and efficient query retrieval through auxiliary indexing, demonstrating significant advancements in decentralized data management.28 0Item Restricted Testing Privacy and Security of Voice Interface Applications in the IoT Era(Temple University, 2024-04-04) Shafei, Hassan Ali; Tan, Chiu C.Voice User Interfaces (VUI) are rapidly gaining popularity, revolutionizing user interaction with technology through the widespread adoption in devices such as desktop computers, smartphones, and smart home assistants, thanks to significant advancements in voice recognition and processing technologies. Over a hundred million users now utilize these devices daily, and smart home assistants have been sold in massive numbers, owing to their ease and convenience in controlling a diverse range of smart devices within the home IoT environment through the power of voice, such as controlling lights, heating systems, and setting timers and alarms. VUI enables users to interact with IoT technology and issue a wide range of commands across various services using their voice, bypassing traditional input methods like keyboards or touchscreens. With ease, users can inquire in natural language about the weather, stock market, and online shopping and access various other types of general information. However, as VUI becomes more integrated into our daily lives, it brings to the forefront issues related to security, privacy, and usability. Concerns such as the unauthorized collection of user data, the potential for recording private conversations, and challenges in accurately recognizing and executing commands across diverse accents, leading to misinterpretations and unintended actions, underscore the need for more robust methods to test and evaluate VUI services. In this dissertation, we delve into voice interface testing, evaluation for privacy and security associated with VUI applications, assessment of the proficiency of VUI in handling diverse accents, and investigation into access control in multi-user environments. We first study the privacy violations of the VUI ecosystem. We introduced the definition of the VUI ecosystem, where users must connect the voice apps to corresponding services and mobile apps to function properly. The ecosystem can also involve multiple voice apps developed by the same third-party developers. We explore the prevalence of voice apps with corresponding services in the VUI ecosystem, assessing the landscape of privacy compliance among Alexa voice apps and their companion services. We developed a testing framework for this ecosystem. We present the first study conducted on the Alexa ecosystem, specifically focusing on voice apps with account linking. Our designed framework analyzes both the privacy policies of these voice apps and their companion services or the privacy policies of multiple voice apps published by the same developers. Using machine learning techniques, the framework automatically extracts data types related to data collection and sharing from these privacy policies, allowing for a comprehensive comparison. Next, researchers studied the voice apps' behavior to conduct privacy violation assessments. An interaction approach with voice apps is needed to extract the behavior where pre-defined utterances are input into the simulator to simulate user interaction. The set of pre-defined utterances is extracted from the skill's web page on the skill store. However, the accuracy of the testing analysis depends on the quality of the extracted utterances. An utterance or interaction that was not captured by the extraction process will not be detected, leading to inaccurate privacy assessment. Therefore, we revisited the utterance extraction techniques used by prior works to study the skill's behavior for privacy violations. We focused on analyzing the effectiveness and limitations of existing utterance extraction techniques. We proposed a new technique that improved prior work extraction techniques by utilizing the union of these techniques and human interaction. Our proposed technique makes use of a small set of human interactions to record all missing utterances, then expands that to test a more extensive set of voice apps. We also conducted testing on VUI with various accents to study by designing a testing framework that can evaluate VUI on different accents to assess how well VUI implemented in smart speakers caters to a diverse population. Recruiting individuals with different accents and instructing them to interact with the smart speaker while adhering to specific scripts is difficult. Thus, we proposed a framework known as AudioAcc, which facilitates evaluating VUI performance across diverse accents using YouTube videos. Our framework uses a filtering algorithm to ensure that the extracted spoken words used in constructing these composite commands closely resemble natural speech patterns. Our framework is scalable; we conducted an extensive examination of the VUI performance across a wide range of accents, encompassing both professional and amateur speakers. Additionally, we introduced a new metric called Consistency of Results (COR) to complement the standard Word Error Rate (WER) metric employed for assessing ASR systems. This metric enables developers to investigate and rewrite skill code based on the consistency of results, enhancing overall WER performance. Moreover, we looked into a special case related to the access control of VUI in multi-user environments. We proposed a framework for automated testing to explore the access control weaknesses to determine whether the accessible data is of consequence. We used the framework to assess the effectiveness of voice access control mechanisms within multi-user environments. Thus, we show that the convenience of using voice systems poses privacy risks as the user's sensitive data becomes accessible. We identify two significant flaws within the access control mechanisms proposed by the voice system, which can exploit the user's private data. These findings underscore the need for enhanced privacy safeguards and improved access control systems within online shopping. We also offer recommendations to mitigate risks associated with unauthorized access, shedding light on securing the user's private data within the voice systems.31 0Item Restricted THE IMPACT OF POPULATION DENSITY AND CYBERSECURITY CHALLENGES IN SMART CITY CREATION(ProQuest, 2024-01-05) Bafail, Ghayda Abdullah; Schaeffer, DonnaWhile the population is growing at a rapid rate worldwide, many people are moving from rural areas to cities when general economic conditions change, looking for good opportunities, better jobs, education, easy life, and better infrastructure. The majority of the population is expected to live in smart cities over the next thirty years. This research contains two parts: quantitative and qualitative. The quantitative part measures the impact of population density on smart-city creation, analyzing thirty-nine countries that have invested in information, communication, and technology (ICT) and ICT goods export for twenty-one years to assess the relationship between population density and smart-city development. The qualitative part briefly discusses the collision of policy, privacy, and ethics in smart cities, which are a top priority in building and developing the smart city, and the main issues policymakers should address when designing smart cities with respect to cybersecurity issues.38 0Item Restricted SOCIAL EXCHANGE THEORY IN THE CONTEXT OF X (TWITTER) AND FACEBOOK SOCIAL MEDIA PLATFORMS WITH A FOCUS ON PRIVCAY CONCERNS AMONG SAUDI STUDENTS(Saudi Digital Library, 2023-12-16) Alqahtani, Sameer Mohammed; Prybutok, Victor RExamining rewards, costs, and comparison levels, the Social Exchange Theory (SET) in sociology underpins our comprehension of self-interest-driven social relationships. Trust, authority, and reciprocity have a substantial impact on these interactions. The Social Exchange Theory (SET) is a valuable lens for understanding human relationships, including online interactions. Social media platforms, such as X (Twitter) and Facebook, have become indispensable communication tools in our daily lives. Nevertheless, due to their user base, they also attract cybercriminals. X (Twitter) offers a variety of security features, such as password protection, two-factor authentication, privacy settings, and app controls, but users must remain vigilant against fraud attempts. Facebook collects vast amounts of private information, which increases the importance of comprehending and implementing security settings. Security awareness is essential for data protection, risk reduction, and conformance with privacy laws. Awareness allows users to manage interactions with security in mind and results in a more secure digital environment, mitigating risks such as identity fraud. Various methodological approaches have allowed the investigation of these two digital phenomena, and the current research contributes to the literature by examining the use of social media and its security settings using a SET lens within a Saudi student environment. This research followed a traditional format for a dissertation, which includes an introduction, literature review, methodology, results, and conclusion with the results section presented the findings from the three essays. The first essay employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology of SET. PRISMA's systematic and exhaustive approach to literature evaluation increases the likelihood of obtaining high-quality, reproducible findings. In the second essay, which focuses on awareness of X’s (Twitter) security settings, a quantitative research approach was utilized. A sample of former and current Saudi students (graduate and undergraduate) at the University of North Texas participated in the investigation. This research provides an empirical examination of the use of X (Twitter) and its security features within this community by employing statistical analysis of the data from respondents. Likewise, the same sample of Saudi students from the University of North Texas was used for the third essay in which the use of Facebook's security settings was examined. Having a consistent sample across both studies enables a comparison and a greater understanding of the security awareness and practices of this group across various social media platforms. The findings across the different studies extend our understanding of the role of culture in privacy and security concerns related to social media.58 0Item Restricted The Use of Text Recognition, Lip Reading, and Object Detection for Protecting Sensitive Information from Shoulder Surfing Attacks(2023-07-19) Aldossari, Marran; Zhang, DongsongThe portability and convenience of laptops have propelled their use in public venues. However, the risk of unauthorized view of sensitive information displayed on these devices, including business data, emails, banking information, online trading information, and private chats, raises privacy concerns. In particular, shoulder-surfing attacks pose a significant threat, whereby individuals can steal sensitive information by looking over one’s shoulder. While researchers have developed various approaches to protect users' screens, such as text modification-based, gesture-based, and external tool-based, those methods have limitations in terms of effectiveness, protection, and usability. To address these limitations, this dissertation proposes, develops, and evaluates three novel methods for protecting sensitive information from shoulder-surfing attacks: detection and labeling (D&L), recognizing and labeling sensitive information in text entry (RLSITE), and “someone is close” (SIC). D&L is a method designed to protect sensitive information while browsing. It works by recognizing and labeling sensitive information in text entry and replacing it with a category label. The labeled and hidden sensitive information is then read to users through their headphones when they click the label. RLSITE is a method designed to protect sensitive information while typing. It works by automatically capturing and interpreting users' lip movements of the sensitive information, then replacing it with a category label and reading it to users through their headphones when they click the label. Finally, the SIC method automatically detects whether someone is close to a user. If so, it will alert the user while labeling the sensitive information and reading it to users through their headphones. The proposed methods have been empirically evaluated in controlled laboratory settings using various measures, including usability, effectiveness, and protection. Evaluation results demonstrate that D&L, RLSITE, and SIC outperform baseline methods in all measures. Furthermore, these innovations have significant practical implications, making them more resistant to shoulder-surfing attacks to browse or enter sensitive content on devices without compromising the usability of these devices.37 0