Permission Based Risk Assessment for Enhancing Privacy of Android Users
Abstract: Mobile applications tend to access data beyond their intended functionality and share this data with third parties for various purposes including marketing, profiling and advertisement. This data also includes user’s personal information and access to this personal information without user’s consent put user’s privacy at risk. User’s Inability to easily find privacy friendly apps and befuddling permission requests paves the way for malicious apps to get access to user’s personal information. Keeping in mind the different level of privacy aware users, we have presented a privacy enforcement framework in this thesis. This framework not only helps user to find alternative privacy friendly apps but also encourage users to review their privacy settings on the smartphone. An app discovery tool is developed to search privacy friendly apps amongst the group with similar functionality. The search results are sorted by privacy friendly score which is calculated using simplified version of risk assessment method known as EBIOS. Threat posed to personal information by various apps are then highlighted and presented to user in an easy-to-understand way before installing the app. We have validated the results of our discovery tool by comparing them to the manual inspection of various functional groups i.e., group of applications with similar functionality. Two list of permissions, one created by subjective and manual analysis of abstract functionality of functional group called expert opinion and other created by our tool based on permissions requested by functional group are compared. Our tool has correctly identified the permissions which are similar to expert opinion.
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