Motivational Factors In Privacy Protection Behaviour Model For Social Networking Sites

Social Networking Sites (SNSs) have exponentially grown over the past decade. They offer a variety of tools that facilitates communication and information sharing. Despite its conveniences, uncontrolled sharing can lead to the loss and exploitation of privacy. Besides, privacy protection behaviour to protect oneself from SNS risks and threats must be emphasizes because more factor may be contribute to privacy protection behaviour, but issues of related to motivational factors of privacy protection behaviour are as of yet, unexplored. This study focuses on the motivational factors of privacy protection behaviour.This study utilised a quantitative research approach through questionnaires. The population of the study comprised of third-year undergraduates from Malaysian public universities. The minimum sample size was determined to be 355 although 497 quesionnaires were distributed. The respondents were selected based on proportional stratified sampling technique. The research instrument was adapted from previous studies, divided into three sections, and validated by a panel of experts from the field of information technology. The data was analysed using SPSS version 22.0 and AMOS version 20.0. The results reveal a moderate level of privacy protection behaviour. The perceived vulnerability was found to be the most salient factor in motivating the adoption of privacy protection behaviour with the mediation of information privacy concern, followed by perceived severity, anonymity of self and others, intrusiveness, self-efficacy and response efficacy. Rewards were also found to be mediated by information privacy concern towards privacy protection behaviour although in a negative fashion. The results attained from the analysis produced a model that predicts the motivational factors of privacy protection behaviour among undergraduates. The model was confirmed to account for 61% of the variance (adjusted R2) in privacy protection behaviour. Expert validation was conducted to better understand the survey results and to obtain validation from experts. Several implications were also drawn from the results of the study. The Protection Motivation Theory (PMT) was tested and expanded upon by the integration of the Hyperpersonal Communication theory (HCT). Through this amalgamation as one mediator, the proposed predictive model is definitive and provides a foundation to guide future research in related fields of study

User Privacy Protection Behavior And Information Sharing In Mobile Health Application

The use of mobile health applications provides a convenient platform to the healthcare sector for conducting self-health monitoring, efficient consultation, health goals achievement, customer’s information data storage, and others. There are growing concerns about privacy in mobile health platforms, particularly when highly sensitive health data is involved. Sharing personal information in mobile health applications does bring risks to the users, which might lead to the leaking of confidential information, and misuse of information. Due to this, protecting one’s information has become essential when using the platform. Therefore, this paper aims to investigate the influence of users’ privacy protection behavior in shaping users’ willingness in sharing information in mobile health applications. This paper adopted a quantitative methodology where data from a survey (N=200) of mobile health application users is analyzed. This study proposed a model that offers understandings on which users’ privacy protection factors could stimulate users to share their information in a mobile health platform. Based on the results, this study concluded that response efficacy has the highest influence on information sharing, followed by vulnerability, self-efficacy, and perceived susceptibility-medical info. The proposed model may benefit other researchers attempting to understand user standpoint’s on data privacy compliance in mobile health application and increase user awareness in the research area

Security and privacy challenges of big data adoption: a qualitative study in telecommunication industry

The telecommunication industry is the leading industry in big data trends as this industry has the most capable infrastructure for big data. However, the adoption of big data in telecommunication services also raises important security and privacy challenges due to the high volume, velocity, and variety of big data characteristics. To address the issue, this study shed light on the security and privacy challenges of big data adoption in the telecommunication industry. This study focuses on investigating the security and privacy challenges for data users in telecommunication services from the lens of the technological, organisational, and environmental (TOE) framework and examines the mitigation strategies to address the privacy and security challenges. This study is conducted using a focus group qualitative methodology. From the perspectives of data users (telecommunication providers), it could be concluded that data management, data privacy, data compliance, and regulatory orchestration challenges are the most pressing concerns in big data adoption. This study offers contributions in presenting a thematic classification of security and privacy challenges and their mitigation strategies for big data adoption in the telecommunication industry. The thematic classification highlights potential gaps for future research in the big data security domain. This study is significant in that it provides empirical evidence for the perspectives of telecommunication data users in addressing privacy and security issues that are related to big data adoption

Unethical internet behaviour among students in high education institutions: a systematic literature review

The modern internet era has several advantages and disadvantages, including the advent of immoral Internet conduct in addition to better, quicker, and increased working capacity in less time. Even though the area of study on unethical Internet activity has advanced, systematic literature reviews from a comprehensive perspective on unethical Internet behaviour among university students are still lacking. As a result, this systematic literature will provide theoretical foundation that address the following research questions: RQ1-How are unethical Internet behaviours among university students classified; RQ2-What are the various theoretical lenses that are used in unethical Internet behaviour research; RQ3-What demographic and risk factors are involved in unethical Internet behaviour research; and RQ4-What are the challenges and research opportunities for unethical Internet behaviour research within university settings? To respond to a formulated set of research questions, a total of 64 publications that were published between 2010 and 2020 underwent a systematic review. The study illustrates how university students’ unethical Internet activity is categorised. This study offers a comprehensive grasp of the factors that affect unethical Internet behaviour and an overview of the theories that have been utilised to explain and forecast unethical Internet behaviours in this sector. This study discusses literature gaps for future research to contribute to human ethical behavioural studies

Data wiping tool: ByteEditor Technique

This Wiping Tool is an anti-forensic tool that is built to wipe data permanently from laptop’s storage. This tool is capable to ensure the data from being recovered with any recovery tools. The objective of building this wiping tool is to maintain the confidentiality and integrity of the data from unauthorized access. People tend to delete the file in normal way, however, the file face the risk of being recovered. Hence, the integrity and confidentiality of the deleted file cannot be protected. Through wiping tools, the files are overwritten with random strings to make the files no longer readable. Thus, the integrity and the confidentiality of the file can be protected. Regarding wiping tools, nowadays, lots of wiping tools face issue such as data breach because the wiping tools are unable to delete the data permanently from the devices. This situation might affect their main function and a threat to their users. Hence, a new wiping tool is developed to overcome the problem. A new wiping tool named Data Wiping tool is applying two wiping techniques. The first technique is Randomized Data while the next one is enhancing wiping technique, known as ByteEditor. ByteEditor is a combination of two different techniques, byte editing and byte deletion. With the implementation of Object�Oriented methodology, this wiping tool is built. This methodology consists of analyzing, designing, implementation and testing. The tool is analyzed and compared with other wiping tools before the designing of the tool start. Once the designing is done, implementation phase take place. The code of the tool is created using Visual Studio 2010 with C# language and being tested their functionality to ensure the developed tool meet the objectives of the project. This tool is believed able to contribute to the development of wiping tools and able to solve problems related to other wiping tools

Enhancing The Randomness Of Symmetric Key Using Genetic Algorithm

The focus of network security is to provide the secure, effective and private communication between the sender and the receiver. To achieve the aim of high security of sending information, the improvement in cryptography is needed to make sure the protection of the information against unauthorized users. Symmetric-key cryptography satisfies the constraint of resources in computational complexity performances, but it offers weak security since it is not resilient against physical compromise. One of the way to overcome the issue is by providing a cryptographic key that is strong, hard to break and almost unpredictable by the intruder. As the advancement of technology in Artificial Intelligence (AI), Genetic Algorithm (GA) is implemented to generate the best-fit key in symmetric-key cryptography. Due to natural selection of GA process, the generated key is found to be the most random and non-repeating as possible. Moreover, the fitness test shows the average fitness value of a generated key increases when the key length increases

Motivational Factors in Privacy Protection Behaviour Model for Social Networking

This study aims to investigate the determinants of the privacy protection behaviour strategies that been employed by users while utilising SNSs. By understanding the determinants of privacy protection will be able to generate awareness that can protect users and allow them to confidently impose their self-control through the execution of privacy protection behaviour strategies. The finding has shown that there was a significant relationship of perceived severity, perceived vulnerability, response efficacy and self-efficacy towards information privacy concern as well as a significant relationship of information privacy concern and privacy protection behaviour strategies. This research is crucial as it serves as a guide that provides instructions and guidelines that help users of SNSs to keep their privacy intact

Motivational Factors in Privacy Protection Behaviour Model for Social Networking

This study aims to investigate the determinants of the privacy protection behaviour strategies that been employed by users while utilising SNSs. By understanding the determinants of privacy protection will be able to generate awareness that can protect users and allow them to confidently impose their self-control through the execution of privacy protection behaviour strategies. The finding has shown that there was a significant relationship of perceived severity, perceived vulnerability, response efficacy and self-efficacy towards information privacy concern as well as a significant relationship of information privacy concern and privacy protection behaviour strategies. This research is crucial as it serves as a guide that provides instructions and guidelines that help users of SNSs to keep their privacy intact