| Title | Privacy Mining of Large-scale Mobile Usage Data |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | He, Yongzhong, Zhao, Xiaojuan, Wang, Chao |
| Conference Name | 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| Keywords | android, compositionality, Data analysis, data mining, data privacy, Human Behavior, human factors, inference mechanisms, ios, iOS (operating system), iOS Security, iOS system, learning (artificial intelligence), machine learning tools, Metrics, mobile computing, mobile devices, mobile network, mobile privacy leakage, mobile traffic data, mobile usage data, Object recognition, password, privacy, privacy analysis, privacy detection model, privacy inference mining, privacy leakage detection, privacy mining, private usage patterns, pubcrawl, resilience, Resiliency, security of data, smart phones |
| Abstract | While enjoying the convenience brought by mobile phones, users have been exposed to high risk of private information leakage. It is known that many applications on mobile devices read private data and send them to remote servers. However how, when and in what scale the private data are leaked are not investigated systematically in the real-world scenario. In this paper, a framework is proposed to analyze the usage data from mobile devices and the traffic data from the mobile network and make a comprehensive privacy leakage detection and privacy inference mining on a large scale of realworld mobile data. Firstly, this paper sets up a training dataset and trains a privacy detection model on mobile traffic data. Then classical machine learning tools are used to discover private usage patterns. Based on our experiments and data analysis, it is found that i) a large number of private information is transmitted in plaintext, and even passwords are transmitted in plaintext by some applications, ii) more privacy types are leaked in Android than iOS, while GPS location is the most leaked privacy in both Android and iOS system, iii) the usage pattern is related to mobile device price. Through our experiments and analysis, it can be concluded that mobile privacy leakage is pervasive and serious. |
| DOI | 10.1109/ICPICS47731.2019.8942559 |
| Citation Key | he_privacy_2019 |
Privacy Mining of Large-scale Mobile Usage Data