MiniChecker: Detecting Data Privacy Risk of Abusive Permission Request Behavior in Mini-Programs
The rising popularity of mini-programs deployed on super-app platforms has drawn significant attention due to their convenience. However, developers' improper handling of data permission application in mini-programs has raised concerns about non-compliance and violations. Unfortunately, existing tools lack the capability to support the construction of a universal function call graph for the mini-program and the literature lacks a comprehensive and systematic study of the abusive issues. To bridge this gap, this paper introduces an automated tool, MiniChecker, to uncover the abusive permission request behavior in mini-programs. It defines five primary categories of abusive issues, namely homepage pop-up, overlaying pop-up, bothering pop-up, repeating pop-up, and looping pop-up, based on the request behavior features. MiniChecker achieves a detection precision rate of 82.4% and a recall rate of 95.3% on our benchmark, and identifies 3,866 risky mini-programs out of 20,000 real-world mini-programs. Our analysis reveals inherent design flaws in the mini-program permission mechanism, and we have shared our findings with several mini-program platforms.
Authors
- W. M. Chen (ORCID: https://orcid.org/0000-0001-7300-9899)
- Deqiang Han (ORCID: https://orcid.org/0000-0001-5603-796X)
- Wuxia Jin (ORCID: https://orcid.org/0000-0002-9137-4635)
- Yin Wang (ORCID: https://orcid.org/0000-0002-2597-208X)
- Yu Zhang (ORCID: https://orcid.org/0009-0005-8097-4976)
- Hao Zhou (ORCID: https://orcid.org/0000-0001-8890-9208)
- Haijun Wang (ORCID: https://orcid.org/0009-0001-3509-3919)
- Shijie Li (ORCID: https://orcid.org/0000-0002-3769-788X)
- Ming Yu Fan (ORCID: https://orcid.org/0000-0002-9327-0987)
- Ting Liu (ORCID: https://orcid.org/0000-0002-7600-0934)
- Jiajia Li
Institutions
- Hong Kong Polytechnic University (HK)
- Xi'an Jiaotong University (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1145/3691620.3695534
- Citations
- 2
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Natural Science Foundation of China