In recent years, people can do most of their personal tasks, such as banking on smart devices like personal computers (PCs). Especially, Malware targeted at personal information stored on mobile are hard to detect and risks from usage patterns are even more difficult. Therefore, a means for easy recognition of the problems and the smartphone usage is necessary. In this paper, we present a personal visual analytics (PVA) system for Android security risk lifelog using app permissions to recognize the risk. Our system stores the security-related personal information on the smartphone device and utilizes it to analyze the security risk lifelog. For the risk analysis, we define security risk scores based on the app and permission statistics. Then, several linked visualizations are designed to present the risk lifelog. We have collected the security lifelog data from eight Android smartphone users and analyzed their security matters. Our PVA system enables Android smartphone users to observe, mitigate the security risk, and eventually understand how Android security risk affects their lives. Moreover, we present a user study to evaluate the PVA system with user feedback.
|Title of host publication||VINCI 2017 - 10th International Symposium on Visual Information Communication and Interaction|
|Editors||Shigeo Takahashi, Jie Li|
|Publisher||Association for Computing Machinery|
|Number of pages||8|
|Publication status||Published - 2017 Aug 14|
|Event||10th International Symposium on Visual Information Communication and Interaction, VINCI 2017 - Bangkok, Thailand|
Duration: 2017 Aug 14 → 2017 Aug 16
|Name||ACM International Conference Proceeding Series|
|Other||10th International Symposium on Visual Information Communication and Interaction, VINCI 2017|
|Period||17/8/14 → 17/8/16|
Bibliographical noteFunding Information:
This work was supported in part by the Ministry of Science, ICT and Future Planning, Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00312, IITP-2017-2016-0-00304) and the grant (R0190-15-2016, Development of Complex Fast Stream Big Data Processing based on In-memory Technology in Distributed Environment) supervised by the IITP (Institute for Information & communications Technology Promotion). Yun Jang is the corresponding author.
© 2017 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications