Implementation of a Cost-Effective Privacy Leakage Detection System for Hosted Programs
Tian Hu, Shangyuan Zhuang, Jiyan Sun, Yinlong Liu, Wei Ma, Yu Chen, Weiyu Liu · 2023
Posting programs to code hosting platforms such as GitHub is common for developers, but it will lead to privacy leakage issues in hosted programs. Though there are some detection methods for privacy leakage, they are difficult to be applied in practice. First, existing works mainly focus on detection algorithms, while ignoring the complete detection system from a holistic perspective. Second, the system will be blocked when acquiring programs because code hosting platforms usually have protection mechanism. Third, high-performance privacy detection algorithms need hardware devices in practice, and their effectiveness in real scenarios has not been verified since there is no public real hosted program privacy dataset.To address the above problems, we implement and commercialize a user-friendly privacy information leakage detection system for actual hosted programs. Firstly, we provide a system frame-work that can automatically complete "program acquisition-privacy detection-alert", allowing subscribers receive alerts if there is a privacy information leakage. Secondly, we propose a novel multi-random crawler scheme that can flexibly cope with the limitations of GitHub when acquiring hosted programs. Thirdly, we skillfully apply a cost-effective detection approach based on fuzzy matching, which can detect the subscriber customized privacy information with high performance. Based on this, we further provide a high-quality dataset obtained in real scenarios. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of our crawler scheme and detection approach in providing a user-friendly system.