A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage.

Area of Science:

• Computer Science
• Information Security
• Mobile Computing

Background:

• Android applications, while offering diverse functionalities, pose privacy risks through permission granting.
• Existing research lacks formal models to quantify application risks and tools to monitor data collection.
• A standardized benchmark for acceptable vs. unacceptable risk levels for Android users is absent.

Purpose of the Study:

• To propose a formalized mathematical model (PRiMo) for quantifying user and system application privacy risks.
• To develop a multifaceted data collector tool (AMoDaC) for monitoring user data collection and associated risks.
• To establish a risk benchmark for Android smartphone users.

Main Methods:

• Developed PRiMo, a privacy model utilizing tree structures and calculus.
• Implemented AMoDaC, a mobile data collector, to analyze app permissions and data access.
• Created a benchmark by comparing PRiMo outcomes with existing testing metrics.

Main Results:

• Tools & Utility/Productivity applications were identified as posing the highest risk.
• 29 users experienced low/acceptable risk, while two users faced medium risk.
• A benchmark was established where risks below 25% are considered safe.

Conclusions:

• PRiMo and AMoDaC effectively address privacy risks in Android smartphones.
• The proposed benchmark provides a clear distinction between acceptable and unacceptable risk levels.
• The developed tools achieved 96.8% effectiveness and accuracy in risk assessment.