A Privacy Protection Approach Based on Android Application's Runtime Behavior Monitor and Control

@article{Wu2018APP,
  title={A Privacy Protection Approach Based on Android Application's Runtime Behavior Monitor and Control},
  author={Fan Wu and Ran Sun and Wenhao Fan and Yuan’an Liu and Feng Liu and Hui Lu},
  journal={Int. J. Digit. Crime Forensics},
  year={2018},
  volume={10},
  pages={95-113},
  url={https://api.semanticscholar.org/CorpusID:49674241}
}
A privacy data protection policy that reflects users' intentions is proposed by extracting and recording the privacy data usage in applications by using an optimized random forest algorithm to reduce the policy training time.
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18 References

A study on static analysis model of mobile application for privacy protection

The purpose of this research is to suggest an analysis system to prevent propagation of harmful applications, which compromise the user’s personal information, by extracting signatures from malicious code samples detected in Android.

AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale

This work presents AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale and indicates that it is capable of scaling to the increasingly large set of available applications.

Aurasium: Practical Policy Enforcement for Android Applications

A novel solution called Aurasium is developed that bypasses the need to modify the Android operating system while providing much of the security and privacy that users desire, and has been tested on three versions of the Android OS, and is freely available.

Using static analysis for automatic assessment and mitigation of unwanted and malicious activities within Android applications

This work proposes a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user, and describes a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.

PScout: analyzing the Android permission specification

An analysis of the permission system of the Android smartphone OS is performed and it is found that a trade-off exists between enabling least-privilege security with fine-grained permissions and maintaining stability of the permissions specification as the Android OS evolves.

Smartphone Dual Defense Protection Framework: Detecting Malicious Applications in Android Markets

A smart phone dual defense protection framework that allows Official and Alternative Android Markets to detect malicious applications among those new applications that are submitted for public release and can achieve a 94.2% and 99.

Overview of information flow tracking techniques based on taint analysis for Android

This paper provides an overview of existing Information flow tracking techniques based on the Taint analysis for android applications and provides in depth analysis of both static and dynamic taint analysis approaches.

Taming Information-Stealing Smartphone Applications (on Android)

A system called TISSA is developed that implements a new privacy mode in smartphones that can empower users to flexibly control in a fine-grained manner what kinds of personal information will be accessible to an application.

These aren't the droids you're looking for: retrofitting android to protect data from imperious applications

Two privacy controls for Android smartphones that empower users to run permission-hungry applications while protecting private data from being exfiltrated are examined, finding that they can successfully reduce the effective permissions of the application without causing side effects for 66% of the tested applications.

AppIntent: analyzing sensitive data transmission in android for privacy leakage detection

The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not, and leverage the unique Android execution model to reduce the search space without sacrificing code coverage.