A survey on Datamining in Cyber Bullying

No Abstrac

An Empirical Study on Android for Saving Non-shared Data on Public Storage

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework

Data Mining and Visualization on Live Chat Data for E-commerce Business

The purpose of this paper is to design, build and evaluate an interactive visualization tool for data analysts to analyze as well as interact with the live chat data from a corporate website for customer relationship management. Sales lead and customer support are the major purposes of the live chat service. Data mining technologies are applied to classify the chat data into categories that can help marketing and sales teams to target their potential customers more accurately and efficiently. By interacting with the web visualization tool, data analysts will have the capability to obtain valuable information about customers' concerns and buying interests on their products and solutions. The results indicate that chat classification achieves higher accuracy on major class "Lead" but lower accuracy on minor classes due to the imbalanced distribution of dataset as well as human bias when manually labeling the training data. Based on the analytic results of chat visualization, data analysts gain the knowledge gap between customers' concern and the information provided on the corporate website, and propose new ideas to improve their digital marketing approaches as well.Master of Science in Information Scienc