iPri: Context-Aware Access Control and Privacy Policy Recommendation
Guoyun Li, Yaozhi Zhang, Xianqi Yu, Yuqing Sun
Abstract
Guoyun Li, Yaozhi Zhang, Xianqi Yu, Yuqing Sun
Abstract
Summary form only given. Intelligent mobile devices are more and more popular in recent years. There are millions of mobile applications, which provide variant functionalities. They bring great convinces to people's lives. However, these applications often request different access requests, which relate with many user privacy information such as identity, contact list or location etc. Some of them are actually not necessary for applications. Although there exist some security applications for privacy protection, in many cases, users do not know how to make privacy setting or even have no knowledge about their privacy leakage. This fact brings user privacy facing serious threat. To solve this problem, we propose a mobile privacy protection framework achieve a fine-grained and context aware permission control. We propose several recommendation algorithms to recommend an appropriate privacy setting when a user requests help. To help a user understand how his/her privacy status, we design and implement a privacy management plug in for Android platform.
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Summary form only given. Intelligent mobile devices are more and more popular in recent years. There are millions of mobile applications, which provide variant functionalities. They bring great convinces to people's lives. However, these applications often request different access requests, which relate with many user privacy information such as identity, contact list or location etc. Some of them are actually not necessary for applications. Although there exist some security applications for privacy protection, in many cases, users do not know how to make privacy setting or even have no knowledge about their privacy leakage. This fact brings user privacy facing serious threat. To solve this problem, we propose a mobile privacy protection framework achieve a fine-grained and context aware permission control. We propose several recommendation algorithms to recommend an appropriate privacy setting when a user requests help. To help a user understand how his/her privacy status, we design and implement a privacy management plug in for Android platform.
Key concepts: Computer science, Permission, Internet privacy, Android (operating system), Privacy software, Access control, Information privacy, Computer security