ORCID Profile
0000-0002-5485-5595
Current Organisation
University of Sydney
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Distributed Computing | Mobile Technologies
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: IEEE
Date: 11-2018
Publisher: ACM
Date: 30-09-2013
Publisher: International World Wide Web Conferences Steering Committee
Date: 18-05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: ACM
Date: 13-05-2019
Publisher: Association for Computing Machinery (ACM)
Date: 28-02-2017
DOI: 10.1145/3007901
Abstract: The increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users’ quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords, or similar apps being made available several times and across erse categories. Market operators maintain antispam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a s le of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% and 95% and recall varying between 38% and 98%. We further show that a limited number of features, in the range of 10--30, generated from app metadata is sufficient to achieve a satisfactory level of performance. On a set of 180,627 apps that were present at the app market during our crawl, our classifier predicts 2.7% of the apps as potential spam. Finally, we perform additional manual verification and show that human reviewers agree with 82% of our classifier predictions.
Publisher: IEEE
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-05-2021
Publisher: Elsevier BV
Date: 11-2023
Publisher: IEEE
Date: 10-2015
Publisher: Elsevier BV
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: Elsevier BV
Date: 05-2023
Publisher: ACM
Date: 11-09-2014
Publisher: Association for Computing Machinery (ACM)
Date: 13-01-2015
Abstract: In this paper, we highlight a potential privacy threat in the current smartphone platforms, which allows any third party to collect a snapshot of installed applications without the user's consent. This can be exploited by third parties to infer various user attributes similar to what is done through tracking. We show that using only installed apps, user's gender, a demographic attribute that is frequently used in targeted advertising, can be instantly predicted with an accuracy around 70%, by training a classifier using established supervised learning techniques.
Publisher: IEEE
Date: 03-2016
Publisher: ACM
Date: 14-11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: ACM
Date: 16-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Wiley
Date: 14-08-2018
DOI: 10.1002/CPE.4736
Publisher: No publisher found
Date: 2017
Publisher: IEEE
Date: 2019
Publisher: ACM
Date: 10-07-2023
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 2023
Publisher: American Geophysical Union (AGU)
Date: 09-2017
DOI: 10.1002/2017GC006934
Publisher: Association for Computing Machinery (ACM)
Date: 03-06-2014
Abstract: Third party apps are an integral component of the smartphone ecosystem. In this paper, we investigate how user traits can be inferred by observing only a single snapshot of installed apps. Using supervised learning methods and minimal external information we show that user traits such as religion, relationship status, spoken languages, countries of interest, and whether or not the user is a parent of small children, can be easily predicted. Using data collected from over 200 smartphone users, specifically the list of installed apps and the corresponding ground truth traits of the users, we show that for most traits we can achieve over 90% precision. Our inference method can be used to provide services such as personalized content delivery or recommender systems for users. We also highlight privacy loss that can occur from unrestricted access to the app lists in popular smartphone operating systems.
Publisher: IEEE
Date: 05-2019
Publisher: ACM
Date: 31-10-2018
Publisher: ACM Press
Date: 2013
Publisher: ACM
Date: 30-10-2023
Start Date: 07-2022
End Date: 06-2025
Amount: $405,000.00
Funder: Australian Research Council
View Funded Activity