Home users are using a wide and increasing range of different technologies, devices, platforms, applications and services every day. In parallel, home users are also installing and using an enormous number of apps, wh...Home users are using a wide and increasing range of different technologies, devices, platforms, applications and services every day. In parallel, home users are also installing and using an enormous number of apps, which collect and share a large amount of data. Users are also often unaware of what information apps collect about them, which is really valuable and sensitive for them. Therefore, users are becoming increasingly concerned about their personal information that is stored in these apps. While most mobile operating systems such as Android and iOS provide some privacy safeguards for users, it is unrealistic to manage and control a large volume of data. Accordingly, there is a need for a new technique, which has the ability to predict many of a user’s mobile app privacy preferences. A major contribution of this work is to utilise different machine learning techniques for assigning users to the privacy profiles that most closely capture their privacy preferences. Applying privacy profiles as default settings for initial interfaces could significantly reduce the burden and frustration of the user. The result shows that it’s possible to reduce the user’s burden from 46 to 10 questions by achieving 86% accuracy, which indicates that it’s possible to predict many of a user’s mobile app privacy preferences by asking the user a small number of questions.展开更多
A wide and increasing range of different technologies, devices, platforms, applications and services are being used every day by home users. In parallel, home users are also experiencing a range of different online th...A wide and increasing range of different technologies, devices, platforms, applications and services are being used every day by home users. In parallel, home users are also experiencing a range of different online threats and attacks. Indeed, home users are increasingly being targeted as they lack the knowledge and awareness about potential threats and how to protect themselves. The increase in technologies and platforms also increases the burden upon a user to understand how to apply and manage security across the differing technologies, operating systems and applications. Different factors such as age, education, age and gender can have an impact on information security management and awareness. This research tries to investigate and examine the effect of gender differences on information security management and online safety for internet users. An online questionnaire has been conducted and collected 434 participants (311 males and 132 females). The results show that there is a significant difference between males and females in four of the eight identified security practices and aspects. The findings show that males are likely to have better information security behaviour and being protected online more than females.展开更多
文摘Home users are using a wide and increasing range of different technologies, devices, platforms, applications and services every day. In parallel, home users are also installing and using an enormous number of apps, which collect and share a large amount of data. Users are also often unaware of what information apps collect about them, which is really valuable and sensitive for them. Therefore, users are becoming increasingly concerned about their personal information that is stored in these apps. While most mobile operating systems such as Android and iOS provide some privacy safeguards for users, it is unrealistic to manage and control a large volume of data. Accordingly, there is a need for a new technique, which has the ability to predict many of a user’s mobile app privacy preferences. A major contribution of this work is to utilise different machine learning techniques for assigning users to the privacy profiles that most closely capture their privacy preferences. Applying privacy profiles as default settings for initial interfaces could significantly reduce the burden and frustration of the user. The result shows that it’s possible to reduce the user’s burden from 46 to 10 questions by achieving 86% accuracy, which indicates that it’s possible to predict many of a user’s mobile app privacy preferences by asking the user a small number of questions.
文摘A wide and increasing range of different technologies, devices, platforms, applications and services are being used every day by home users. In parallel, home users are also experiencing a range of different online threats and attacks. Indeed, home users are increasingly being targeted as they lack the knowledge and awareness about potential threats and how to protect themselves. The increase in technologies and platforms also increases the burden upon a user to understand how to apply and manage security across the differing technologies, operating systems and applications. Different factors such as age, education, age and gender can have an impact on information security management and awareness. This research tries to investigate and examine the effect of gender differences on information security management and online safety for internet users. An online questionnaire has been conducted and collected 434 participants (311 males and 132 females). The results show that there is a significant difference between males and females in four of the eight identified security practices and aspects. The findings show that males are likely to have better information security behaviour and being protected online more than females.
