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. Coarse-grained access control systems such as the Android permission system offer all-or-nothing access to photos stored on smartphones, and users are unaware of the exact behavior of installed apps. Our analysis finds that 82 of the top 200 free apps in a popular Android app store have complete access to stored photos and network on a user's smartphone, which indicates possible private photo leakage. In addition, our user survey reveals that 87.5 of the 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 locked or not) and app-running status (e.g., app in the background). Evaluations demonstrate that PhotoSafer can accurately identify private photos in real time. The efficacy and efficiency of the implemented prototype system show the potential for practical use.
Work has been done on permission models @cite_6 @cite_35 @cite_19 @cite_14 @cite_3 , which found that users usually do not know how apps access sensitive resources and how such access is managed. @cite_28 showed that private information is more likely to be leaked when users are unaware of the purpose for requesting a specific sensitive resource.
@cite_35 @cite_14 @cite_28 @cite_3 @cite_6 @cite_19
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Smartphone operating systems warn users when third-party applications try to access sensitive functions or data. However, all of the major smartphone platforms warn users about different application actions. To our knowledge, their selection of warnings was not grounded in user research; past research on mobile privacy has focused exclusively on the risks pertained to sharing location. To expand the scope of smartphone security and privacy research, we surveyed 3,115 smartphone users about 99 risks associated with 54 smartphone privileges. We asked participants to rate how upset they would be if given risks occurred and used this data to rank risks by levels of user concern. We then asked 41 smartphone users to discuss the risks in their own words; their responses confirmed that people find the lowest-ranked risks merely annoying but might seek legal or financial retribution for the highest-ranked risks. In order to determine the relative frequency of risks, we also surveyed the 3,115 users about experiences with "misbehaving" applications. Our ranking and frequency data can be used to guide the selection of warnings on smartphone platforms. In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their mobile app privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an app. Instead it relies on static code analysis to determine the purpose for which an app requests each of its permissions, distinguishing for instance between apps relying on particular permissions to deliver their core functionality and apps requesting these permissions to share information with advertising networks or social networks. Using privacy preferences that reflect people’s comfort with the purpose for which different apps request their permissions, we use clustering techniques to identify privacy profiles. A major contribution of this work is to show that, while people’s mobile app privacy preferences are diverse, it is possible to identify a small number of privacy profiles that collectively do a good job at capturing these diverse preferences. The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a four-week study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather their personal context and to measure their privacy preferences by varying app name and purpose of data collection. Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given. We found that the purpose for data access was the predominant factor affecting users' choices. In our study the purpose condition vary from being not specified, to vague to be very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown participants were more willing to disclose when the purpose for requesting the information appeared to be beneficial to them, and shared the least when the purpose for data access was solely beneficial to developers. Mobile devices are playing an increasingly intimate role in everyday life. However, users can be surprised when informed of the data collection and distribution activities of apps they install. We report on two studies of smartphone users in western European countries, in which users were confronted with app behaviors and their reactions assessed. Users felt their personal space had been violated in "creepy" ways. Using Altman's notions of personal space and territoriality, and Nissenbaum's theory of contextual integrity, we account for these emotional reactions and suggest that they point to important underlying issues, even when users continue using apps they find creepy. Android's permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application's permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17 of participants paid attention to permissions during installation, and only 3 of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.