Imagining, Studying and Realising A Less Harmful App Ecosystem
- URL: http://arxiv.org/abs/2205.00774v1
- Date: Mon, 2 May 2022 09:45:37 GMT
- Title: Imagining, Studying and Realising A Less Harmful App Ecosystem
- Authors: Konrad Kollnig, Siddhartha Datta, Nigel Shadbolt
- Abstract summary: We investigate mobile app extensions, a previously underexplored concept to study and address digital harms within mobile apps.
We present a ready-to-use implementation for Android as a result of significant and careful system development.
Our method provides a versatile foundation for a range of follow-up research into digital harms in mobile apps.
- Score: 10.65724536340206
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Desktop browser extensions have long allowed users to improve their
experience online and tackle widespread harms on websites. So far, no
equivalent solution exists for mobile apps, despite the fact that individuals
now spend significantly more time on mobile than on desktop, and arguably face
similarly widespread harms.
In this work, we investigate mobile app extensions, a previously
underexplored concept to study and address digital harms within mobile apps in
a decentralised, community-driven way. We analyse challenges to adoption of
this approach so far, and present a ready-to-use implementation for Android as
a result of significant and careful system development. Through a range of case
studies, we demonstrate that our implementation can already reduce (though not
completely eliminate) a wide range of harms - similarly as browser extensions
do on desktops.
Our method provides a versatile foundation for a range of follow-up research
into digital harms in mobile apps that has not previously been possible, given
that browser extensions have long been a fruitful foundation for research
studies on desktops. In other words, our system tries to address the gap of a
focus on desktop interventions in previous research.
Related papers
- Assessing Web Fingerprinting Risk [2.144574168644798]
Browser fingerprints are device-specific identifiers that enable covert tracking of users even when cookies are disabled.
Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk.
We provide the first study of browser fingerprinting which addresses the limitations of prior work.
arXiv Detail & Related papers (2024-03-22T20:34:41Z) - Software Engineering for OpenHarmony: A Research Roadmap [50.56072657598223]
Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS.
OpenHarmony, a newly open-sourced mobile platform, has rarely been considered.
We present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony.
arXiv Detail & Related papers (2023-11-02T15:27:09Z) - Verifying Learning-Based Robotic Navigation Systems [61.01217374879221]
We show how modern verification engines can be used for effective model selection.
Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior.
Our work is the first to demonstrate the use of verification backends for recognizing suboptimal DRL policies in real-world robots.
arXiv Detail & Related papers (2022-05-26T17:56:43Z) - Brief View and Analysis to Latest Android Security Issues and Approaches [0.0]
We conduct a wide range of analysis, including latest malwares, Android security features, and approaches.
We also provide some finding when we are gathering information and carrying on experiments.
arXiv Detail & Related papers (2021-09-02T09:34:11Z) - Demystifying Removed Apps in iOS App Store [0.0]
This paper takes the initiative to conduct a large-scale and longitudinal study of removed apps in the iOS app store.
Our analysis reveals that although most of the removed apps are low-quality apps, a number of them are quite popular.
arXiv Detail & Related papers (2021-01-13T14:34:26Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - 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) - Federated and continual learning for classification tasks in a society
of devices [59.45414406974091]
Light Federated and Continual Consensus (LFedCon2) is a new federated and continual architecture that uses light, traditional learners.
Our method allows powerless devices (such as smartphones or robots) to learn in real time, locally, continuously, autonomously and from users.
In order to test our proposal, we have applied it in a heterogeneous community of smartphone users to solve the problem of walking recognition.
arXiv Detail & Related papers (2020-06-12T12:37:03Z) - BADGR: An Autonomous Self-Supervised Learning-Based Navigation System [158.6392333480079]
BadGR is an end-to-end learning-based mobile robot navigation system.
It can be trained with self-supervised off-policy data gathered in real-world environments.
BadGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles.
arXiv Detail & Related papers (2020-02-13T18:40:21Z) - SeMA: Extending and Analyzing Storyboards to Develop Secure Android Apps [0.0]
SeMA is a mobile app development methodology that builds on existing mobile app design artifacts such as storyboards.
An evaluation of the effectiveness of SeMA shows the methodology can detect and help prevent 49 vulnerabilities known to occur in Android apps.
arXiv Detail & Related papers (2020-01-27T20:10:52Z)
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.