Privacy risk metrics and visualization for mobile social networks (MSNs)

The contraption of smartphone technology has become very useful to our daily activities, most especially in terms of networking and communication, through the top five (5) most popularly used social network applications such as Facebook, Instagram, Twitter, Snapchat and LinkedIn. They create a platform where users may access, publish and share content generated by them in other to enhance their social interactions. Specifically, increased use of smartphones capable of running MSNs applications gain access to user’s private information by requesting sets of permissions during installation. Hence, the lack of awareness has led to the pervasive use of background information which enable applications to be aware of a user’s location and preferences. The main objective of this dissertation is to improve MSN user’s awareness on potential privacy risk after installing an application. A privacy risk metric was proposed to quantify and visualize the risk in an application. Over the years, numerous research studies have been reported on how to limit privacy leakage and improve user’s awareness. However most of these studies provide relatively low privacy satisfaction and concentrated on a single pool of users. This dissertation designs a privacy risk metrics with the use of the top 30 most dangerously requested permissions in the top five (5) MSN application, in which we categorized the various attacks on network and application in to various risk dimension by using the Confidentiality, Integrity and Availability (CIA) to quantify and visualize the total risk magnitude implication based on the permissions requested by each of the five (5) apps after installation through a meter which makes the privacy risk interpretation easier to understand. We conducted a survey by distributing questionnaires among Universiti Putra Malaysia (UPM) students with 147 respondents to know their level of permission comprehension when installing an application and also their preferred display style for risk visualization. However, from the results gotten we discovered that most of the users do not really understand the permission been requested by an application and so, 51.7% of the respondent choose the meter which helps in visualizing the privacy risk magnitude and also enables them to become privacy conscious.

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