Analysis of Longitudinal Changes in Privacy Behavior of Android Applications

• URL: http://arxiv.org/abs/2112.14205v1
• Date: Tue, 28 Dec 2021 16:21:31 GMT
• Title: Analysis of Longitudinal Changes in Privacy Behavior of Android Applications
• Authors: Alexander Yu, Yuvraj Agarwal, Jason I. Hong
• Abstract summary: 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.
• Score: 79.71330613821037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Privacy concerns have long been expressed around smart devices, and the concerns around Android apps have been studied by many past works. Over the past 10 years, we have crawled and scraped data for almost 1.9 million apps, and also stored the APKs for 135,536 of them. In this paper, we examine the trends in how Android apps have changed over time with respect to privacy and look at it from two perspectives: (1) how privacy behavior in apps have changed as they are updated over time, (2) how these changes can be accounted for when comparing third-party libraries and the app's own internals. To study this, 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. However, we observe that in the current state of Android apps, there has not been enough of an improvement in terms of privacy and many issues still need to be addressed.

Related papers

• A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends [20.714617724462393]
  We examine what users have been writing about privacy along multiple dimensions: time, countries, app types, diverse privacy topics, and even across a spectrum of emotions. We find that although privacy reviews come from more than 200 countries, 33 countries provide 90% of privacy reviews. Surprisingly, we uncover that it is not uncommon for reviews that discuss privacy to be positive (32%); many users express pleasure about privacy features within apps or privacy-focused apps.
  arXiv  Detail & Related papers  (2024-03-04T18:21:56Z)
• ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels [2.457872341625575]
  We introduce the Automated Privacy Label Analysis System (ATLAS) ATLAS identifies possible discrepancies between mobile app privacy policies and their privacy labels. We find that, on average, apps have 5.32 such potential compliance issues.
  arXiv  Detail & Related papers  (2023-05-24T05:27:22Z)
• Tight Auditing of Differentially Private Machine Learning [77.38590306275877]
  For private machine learning, existing auditing mechanisms are tight. They only give tight estimates under implausible worst-case assumptions. We design an improved auditing scheme that yields tight privacy estimates for natural (not adversarially crafted) datasets.
  arXiv  Detail & Related papers  (2023-02-15T21:40:33Z)
• 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)
• The Privacy Onion Effect: Memorization is Relative [76.46529413546725]
  We show an Onion Effect of memorization: removing the "layer" of outlier points that are most vulnerable exposes a new layer of previously-safe points to the same attack. It suggests that privacy-enhancing technologies such as machine unlearning could actually harm the privacy of other users.
  arXiv  Detail & Related papers  (2022-06-21T15:25:56Z)
• Goodbye Tracking? Impact of iOS App Tracking Transparency and Privacy Labels [25.30364629335751]
  Apple introduced two significant changes with iOS 14: App Tracking Transparency (ATT), a mandatory opt-in system for enabling tracking on iOS, and Privacy Nutrition Labels. This paper addresses the impact of these changes on individual privacy and control by analysing two versions of 1,759 iOS apps from the UK App Store. We find that Apple itself engages in some forms of tracking and exempts invasive data practices like first-party tracking and credit scoring.
  arXiv  Detail & Related papers  (2022-04-07T16:32:58Z)
• SPAct: Self-supervised Privacy Preservation for Action Recognition [73.79886509500409]
  Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. We present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels.
  arXiv  Detail & Related papers  (2022-03-29T02:56:40Z)
• Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps [25.30364629335751]
  We present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. Third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law.
  arXiv  Detail & Related papers  (2021-09-28T13:40:32Z)
• An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy [1.8033500402815792]
  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.
  arXiv  Detail & Related papers  (2020-10-11T02:00:36Z)
• 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)

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.