Name
Abstract | ||
|---|---|---|
Nowadays many people store photos in smartphones. Many of the photos contain sensitive, private information, such as a photocopy of driver's license and credit card. An arising privacy concern is with the unauthorized accesses to such private photos by installed apps. The Android permission system offers all-or-nothing access to photos stored on smartphones, which is still coarse-grained control and makes users unaware of the exact behavior of installed apps. Our analysis found that 82% of the top 200 free apps have complete access to stored photos and network on a user's smartphone. In addition, our user survey revealed that 87.5% of 112 respondents are not aware that certain apps can access their photos without informing users, and all the respondents believe that the stored photos on their smartphones contain different types of private information. Hence, we propose PhotoSafer, a content-based, context-aware private photo protection system for Android phones. PhotoSafer can detect private photos based on photo content with a well-trained deep convolutional neural network, and control access to photos based on system status (e.g., screen is locked) and app running status (e.g., background). Evaluations demonstrate that PhotoSafer can accurately identify private photos in real time. The effectiveness and efficiency of the implemented prototype system show the potential for practical use. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PAC.2018.00008 | 2018 IEEE Symposium on Privacy-Aware Computing (PAC) |
Keywords | DocType | Volume |
Mobile Phone,Photo,Privacy | Conference | abs/1810.01046 |
ISBN | Citations | PageRank |
978-1-5386-8443-6 | 0 | 0.34 |
References | Authors | |
24 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ang Li | 1 | 501 | 36.38 |
| David Darling | 2 | 0 | 0.34 |
| Qing-Hua Li | 3 | 1563 | 88.15 |