Conference paper Open Access

ARM: ANN-based ranking model for privacy and security analysis in smartphone ecosystems

Hatamian, Majid; Serna, Jetzabel

Smartphone ecosystems are considered as a unique source due to the large number of apps which in turn makes an extensive use of personal data. Currently, there is no privacy and security preservation mechanism in smartphone ecosystems to enable users to compare apps in terms of privacy and security protection level, and to alarm them regarding the invasive issues (in terms of privacy and security) of apps before installing them. In this paper, we exploit user comments on app stores as an important source to extract privacy and security invasive (PSI) claims corresponding to apps. Thus, we propose an artificial neural network (ANN)-based ranking model (ARM) in order to classify user comments with privacy and security concerns. Our ranking model is based on three main features namely privacy and security, sentiment, and lifetime analyses as the input of the ranking model along with a novel mathematical formulation in such a way as to maximise the differentiation between comments. The performance results show that ARM is able to classify and predict PSI user comments with accuracy as high as 93.3%. Our findings confirm that due to the functionality of ARM, it has the potential to be widely adopted in smartphone ecosystems.

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