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

• URL: http://arxiv.org/abs/2010.06371v1
• Date: Sun, 11 Oct 2020 02:00:36 GMT
• Title: An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy
• Authors: Debjyoti Mukherjee, Alireza Ahmadi, Maryam Vahdat Pour, Joel Reardon
• Abstract summary: This study examines the privacy and security concerns of users using reviews in the Google Play Store. We analyzed 2.2M reviews from the top 539 apps of this Android market. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter.
• Score: 1.8033500402815792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• A Large-Scale Empirical Study of COVID-19 Contact Tracing Mobile App Reviews [2.2957483176038584]
  We collected reviews of 35 COVID-19 contact tracing apps developed by 34 countries across the globe. We group the app reviews into the following geographical regions: Asia, Europe, North America, Latin America, Africa, Middle East, and Australasia. While privacy could be a concern with such apps, we only find privacy-related topics in Australasia, North America, and Middle East. Users frequently complained about the lack of features, user interface and the negative impact of such apps on their mobile batteries.
  arXiv  Detail & Related papers  (2024-04-28T09:31:36Z)
• User Driven Functionality Deletion for Mobile Apps [10.81190733388406]
  Evolving software with an increasing number of features is harder to understand and thus harder to use. Too much functionality can easily impact usability, maintainability, and resource consumption. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews.
  arXiv  Detail & Related papers  (2023-05-30T19:56:54Z)
• Privacy Explanations - A Means to End-User Trust [64.7066037969487]
  We looked into how explainability might help to tackle this problem. We created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required. Our findings reveal that privacy explanations can be an important step towards increasing trust in software systems.
  arXiv  Detail & Related papers  (2022-10-18T09:30:37Z)
• Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play [13.658284581863839]
  Google Play's policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. We examine install-incentivizing apps through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers.
  arXiv  Detail & Related papers  (2022-02-09T16:54:27Z)
• Analysis of Longitudinal Changes in Privacy Behavior of Android Applications [79.71330613821037]
  In this paper, we examine the trends in how Android apps have changed over time with respect to privacy. We examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers. We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy.
  arXiv  Detail & Related papers  (2021-12-28T16:21:31Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis [67.58267006314415]
  We present a large-scale analysis on ad-related user feedback from App Store and Google Play. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage. Some ad issue types are addressed more quickly by developers than other ad issues.
  arXiv  Detail & Related papers  (2020-08-22T05:38:24Z)
• Automating App Review Response Generation [67.58267006314415]
  We propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4.
  arXiv  Detail & Related papers  (2020-02-10T05:23:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.