Analyzing Android App Privacy With GP-PP Model | Zendy

Nishtha Kesswani | Zendy; Hongbo Lyu | Zendy; Zuopeng Zhang | Zendy

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Analyzing Android App Privacy With GP-PP Model

Author(s) -

Nishtha Kesswani,

Hongbo Lyu,

Zuopeng Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2850060

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Mobile devices have become the major platforms of the Internet of Things applications for industrial enterprises. The ever-increasing number of Android Phone users has raised great concerns regarding the privacy issues related to the use of Android Apps. Freely downloadable apps requesting a large number of permissions have resulted in severe privacy concerns. While granting the permissions, users usually do not read their details or are unable to judge an app based on the permissions requested. In this paper, we address the privacy issues by categorizing app permissions into privacy invasive and generic permissions and validating the classification using the Naïve Bayes classifier.

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