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An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy

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arxiv 2010.06371 v1 pith:HHYXYDSJ submitted 2020-10-11 cs.CR cs.AIcs.LG

classification cs.CRcs.AIcs.LG
keywords appsreviewsusersprivacysecurityconcernsaffectdifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Application markets provide a communication channel between app developers and their end-users in form of app reviews, which allow users to provide feedback about the apps. Although security and privacy in mobile apps are one of the biggest issues, it is unclear how much people are aware of these or discuss them in reviews. In this study, we explore the privacy and security concerns of users using reviews in the Google Play Store. For this, we conducted a study by analyzing around 2.2M reviews from the top 539 apps of this Android market. We found that 0.5\% of these reviews are related to the security and privacy concerns of the users. We further investigated these apps by performing dynamic analysis which provided us valuable insights into their actual behaviors. Based on the different perspectives, we categorized the apps and evaluated how the different factors influence the users' perception of the apps. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter. We also found that sending out the location can affect the users' thoughts about the app. The other factors do not directly affect the privacy and security concerns for the users.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SAGE: A Context-Aware Approach for Mining Privacy Requirements Relevant Reviews from Mental Health Apps

    cs.SE 2025-07 reject novelty 5.0 of 10

    SAGE combines domain-specific NLI hypotheses with a zero-shot GPT classifier to identify privacy-related app reviews, reporting F1 0.85 on 1,376 labels and extracting 748 new privacy reviews.

  2. CMER: A Context-Aware Approach for Mining Ethical Concern-related App Reviews

    cs.SE 2025-07 conditional novelty 4.0 of 10

    A hybrid NLI-plus-LLM pipeline with finance-specific hypotheses extracted 2,178 manually validated privacy/security reviews from mobile investment app reviews.

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