Automated identification of sensitive data via flexible user requirements

Abstract

Protecting sensitive data in web and mobile applications requires identifying sensitive data, which typically needs intensive manual efforts. In addition, deciding sensitive data subjects to users’ requirements and the application context. Existing research efforts on identifying sensitive data from its descriptive texts focus on keyword/phrase searching. These approaches can have high false positives/negatives as they do not consider the semantics of the descriptions. In this paper, we propose S3, an automated approach to identify sensitive data based on user requirements. It considers semantic, syntactic and lexical information comprehensively, aiming to identify sensitive data by the semantics of its descriptive texts. We introduce the notion concept space to represent the user’s notion of privacy, by which our approach can support flexible user requirements in defining sensitive data. Our approach is able to learn users’ preferences from readable concepts initially provided by users, and automatically identify related sensitive data. We evaluate our approach on over 18,000 top popular applications from Google Play Store. S3 achieves an average precision of 89.2%, and average recall 95.8% in identifying sensitive data.