Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identifying sensitive user inputs is a prerequisite for privacy protection in mobile applications. When it comes to today’s program analysis systems, hover, only those data that go through ll-defined system Application Program Interface (system controlled resources) can be automatically labeled. ,  show that this conventional approach is far from adequate, as most sensitive inputs are actually entered by the user at an app’s runtime. ,  inspect 13,072 top apps from Google Play, and find that 38.69% of them involve sensitive user inputs. Just like system controlled resources, these data are also exposed to a series of privacy leakage threats. For these sensitive user inputs, manually marking them involves a lot of efforts, impeding a large-scale, automated analysis of apps to defend against potential privacy leakage. To address this important issue,  present  UIPicker , an adaptable framework for automatic identification of sensitive user inputs as the first step. UIPicker is designed to detect the semantic information within the application layout resources and the program code, and further analyze it for the locations where security-critical information may show up. This approach can support a variety of existing security analysis on mobile apps.  evaluate our approach over randomly selected popular apps on Google Play. UIPicker is able to accurately label sensitive user inputs most of the time, with 94.0% precision and 96.0% recall.
['Yuhong Nan', 'Zhemin Yang', 'Min Yang', 'Shunfan Zhou', 'Yuan Zhang', 'Guofei Gu', 'XiaoFeng Wang', 'Limin Sun']
I am writing a research paper on a idea Identifying User-Input Privacy in Mobile Applications at a Large Scale
[{"from": "human", "value": "I am writing a research paper on a idea Identifying User-Input Privacy in Mobile Applications at a Large Scale"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identifying sensitive user inputs is a prerequisite for privacy protection in mobile applications. When it comes to today\u2019s program analysis systems, hover, only those data that go through ll-defined system Application Program Interface (system controlled resources) can be automatically labeled. ,  show that this conventional approach is far from adequate, as most sensitive inputs are actually entered by the user at an app\u2019s runtime. ,  inspect 13,072 top apps from Google Play, and find that 38.69% of them involve sensitive user inputs. Just like system controlled resources, these data are also exposed to a series of privacy leakage threats. For these sensitive user inputs, manually marking them involves a lot of efforts, impeding a large-scale, automated analysis of apps to defend against potential privacy leakage. To address this important issue,  present  UIPicker , an adaptable framework for automatic identification of sensitive user inputs as the first step. UIPicker is designed to detect the semantic information within the application layout resources and the program code, and further analyze it for the locations where security-critical information may show up. This approach can support a variety of existing security analysis on mobile apps.  evaluate our approach over randomly selected popular apps on Google Play. UIPicker is able to accurately label sensitive user inputs most of the time, with 94.0% precision and 96.0% recall."}]