๐ค AI Summary
ๆฌๆ้ๅฏน้ป่ฎค็งปๅจๅบ็จไธญ็้็งๆงๅถ้ฎ้ข๏ผๆๅบไบไธ็งๅบไบ็จๆทๅไธไธๆ็ๆ้ๆฒป็ๆกๆถ๏ผ้่ฟๅผๅ
ฅโๆ้ๆๆโ้้กนๅๅ ๆ่ฏๅ็ณป็ปๆฅๆๅ็จๆท็็่งฃๅๅณ็ญๆธ
ๆฐๅบฆใ
๐ Abstract
Mobile operating systems provide runtime permission controls intended to improve user control over sensitive data. However, default or pre-installed applications are deeply integrated into the system, may operate with elevated privileges, and are difficult for users to scrutinize. Existing permission models generally grant persistent or temporary access for an application session without distinguishing among individual features, leaving users uncertain about when and why data are accessed. This paper presents a context-sensitive, user-focused permission governance framework for default mobile applications. It introduces a feature-based authorization option, "Allow When Needed," that restricts access to the functionality requiring the data rather than the entire application session. A weighted scoring system estimates the privacy implications of user choices based on permission sensitivity and authorization type. A web-based simulation platform was developed to model 30 realistic permission-request situations across six commonly used default application types and support controlled early-stage evaluation before native implementation. The exploratory assessment combined a cross-sectional survey of 104 respondents examining permission awareness and behavior with formative usability testing involving eight participants interacting with the prototype. Survey findings indicate that users do not consistently examine default-application permissions and prefer contextual explanations before granting access. The results provide preliminary evidence that context-aware permission governance can improve user understanding and decision clarity. This simulation-based study represents an initial step toward evaluating feature-level authorization and privacy-feedback mechanisms before native mobile deployment.