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Mini-app is an emerging form of mobile application that combines webtechnology with native capabilities. Its features, e.g., no need to downloadand no installation, have made it popular rapidly. However, privacy issues thatviolate the laws or regulations are breeding in the swiftly expanding mini-appecosystem. The consistency between what the mini-app does about the data in theprogram code and what it declares in its privacy policy description isimportant. But no work has systematically investigated the privacy problem ofthe mini-app before. In this paper, to our best knowledge, we are the first toconduct the compliance detection of data practice and policy description inmini-apps. In this paper, we first customize a taint analysis method based ondata entity dependency network to adapt to the characteristics of theJavaScript language in the mini-apps. Then, we transform data types and dataoperations to data practices in program codes and privacy policies, so as tofinish a fine-grained consistency matching model.We crawl 100,000 mini-apps onWeChat client in the wild and extract 2,998 with a privacy policy. Among them,only 318 meet the consistency requirements, 2,680 are inconsistent, and theproportion of inconsistencies is as high as 89.4%. The inconsistency in themini-app is very serious. Based on 6 real-world cases analyzed, in order toreduce this potential data leakage risk, we suggest that the developer shouldreduce the collection of irrelevant information and the straightforward use oftemplates, and the platform should provide data flow detection tools andprivacy policy writing support.

Do as You Say: Consistency Detection of Data Practice in Program Code and Privacy Policy in Mini-App