Poster: What Can Review with Security Concern Tell Us before Installing Apps?
Pages 70 - 71
Abstract
Providing pertinent information to potential users about security concerns drawn from user feedback is essential to enhance the security level of the entire mobile ecosystem. According to a usable security survey, star ratings and reviews can help users’ download and permission decisions. However, many apps have security problems with a high star rating. To confirm this problem, we build a dataset by collecting 56,439,878 star ratings and reviews for 8,999 popular game apps on Google Play Store. This study proposes a model using an active learning method that distinguishes Review with Security Concern (RSC) from user reviews. As a result, we find the star rating and RSC have a very weak correlation (Pearson correlation value -0.2949). Thus, it shows that the current star rating system does not reflect users’ security concerns and needs improvements to deliver a more practical metric before installing an app by users.
References
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2022. Review with Security Concern Dataset. https://ocslab.hksecurity.net/Datasets/review-with-security-concern-dataset.
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2022. Top Grossing Apps (2022). https://www.businessofapps.com/data/top-grossing-apps/.
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Deguang Kong, Lei Cen, and Hongxia Jin. 2015. Autoreb: Automatically understanding the review-to-behavior fidelity in android applications. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 530–541.
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Jialiu Lin, Shahriyar Amini, Jason I Hong, Norman Sadeh, Janne Lindqvist, and Joy Zhang. 2012. Expectation and purpose: understanding users’ mental models of mobile app privacy through crowdsourcing. In Proceedings of the 2012 ACM conference on ubiquitous computing. 501–510.
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Ngoc Thi Nguyen, Agustin Zuniga, Hyowon Lee, Pan Hui, Huber Flores, and Petteri Nurmi. 2020. (m) ad to see me? intelligent advertisement placement: Balancing user annoyance and advertising effectiveness. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 2 (2020), 1–26.
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Bingyu Shen, Lili Wei, Chengcheng Xiang, Yudong Wu, Mingyao Shen, Yuanyuan Zhou, and Xinxin Jin. 2021. Can Systems Explain Permissions Better? Understanding Users’ Misperceptions under Smartphone Runtime Permission Model. In 30th USENIX Security Symposium (USENIX Security 21). 751–768.
Index Terms
- Poster: What Can Review with Security Concern Tell Us before Installing Apps?
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Published In
September 2022
538 pages
ISBN:9781450394239
DOI:10.1145/3544793
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 April 2023
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- Refereed limited
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UbiComp/ISWC '22
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UbiComp/ISWC '22: The 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing
September 11 - 15, 2022
Cambridge, United Kingdom
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Overall Acceptance Rate 764 of 2,912 submissions, 26%
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