Data mining has been increasingly gathering attention in recent years. That is why there are plenty of relevant thesis topics in data mining. Consequently, in order to choose a good topic, one has to consider several aspects regarding the area, techniques, and purpose of the study, starting with the choice between theory and practice, or, perhaps, concentrate on both. What is the most important is that the topic should appeal to the student as there are so many possible thesis topics in data mining that he or she is likely to get confused while making a proper choice of the most relevant one.
“So what should I do my thesis about?” Look what you can explore in your thesis.
Data Mining and User Privacy
Privacy protection has been a concern for public policy makers for decades. The development of more complex methods of collecting and analyzing personal information has made privacy a major issue for public and government domains. Specifically, the rise of data mining has put the issue of privacy in a new light. Data mining refers to the process of discovering patterns and knowledge from massive amounts of data. The process lies in employing different prospectives to data analysis and generating useful information. It has been applied to a variety of domains, such as Web research, scientific discovery, business intelligence, and homeland security.
Although the information that data mining can discover may be valuable and have a variety of applications and even help to fight terrorism, it poses a serious threat to privacy. Such technology can be easily abused especially taking into consideration the fact that many customers are not aware that their purchases and the publicly shared information are mined. For instance, when one fills out an application for a loan, the information the person submits will most likely be put in a database. Moreover, the information will probably not stay in one place; it can be sold or, either intentionally or not, put on the Internet and become available to everyone. Furthermore, the violation of individual’s privacy can occur due to other factors, for instance, data mining tools may access private information without authorization, discover undesired information, or use the data for purposes that differ from the one for which it was collected.
However, there might be a resolution for the issue that lies in the data mining subdivision called privacy preserving data mining. Its objective is to protect sensitive information from disclosure and, at the same time, preserve its utility. The technology prevents sensitive data, such as telephone and ID card numbers, from being directly used for mining as well as excludes the results the disclosure of which would lead to a violation of privacy. Another approach to eliminating privacy concerns is to mine data from distributed sources. In this case, there will be no need to disclose the data that will eliminate the massive databases, which are the main reason behind the concerns. According to this approach, the individual data is split among different sites. As a result, it will require compromising several databases in order to obtain enough information about an individual.
All in all, even though data mining can be a useful tool for detecting fraud, assessing product retailing, and identifying terrorist activities, it poses a threat of invading individual privacy. There are, however, several ways to minimize this disadvantage, such as the use of privacy preserving data mining and mining data from distributed sources.
- Dean, M., Payne, D., & Landry, B. (2016). Data mining: an ethical baseline for online privacy policies. Journal Of Enterprise Information Management, 29(4), 482-504. http://dx.doi.org/10.1108/jeim-04-2014-0040
- Fletcher, S., & Islam, M. (2014). Measuring Information Quality for Privacy Preserving Data Mining. International Journal Of Computer Theory And Engineering, 7(1), 21-28. http://dx.doi.org/10.7763/ijcte.2015.v7.924
- Nathiya,, S., Kuyin, C., & Sundari, j. (2016). Providing Multi Security In Privacy Preserving Data Mining. International Journal Of Engineering And Computer Science. http://dx.doi.org/10.18535/ijecs/v4i12.50
- Wu, X., Zhu, X., Wu, G., & Ding, W. (2014). Data mining with big data. IEEE Transactions On Knowledge And Data Engineering, 26(1), 97-107. http://dx.doi.org/10.1109/tkde.2013.109
- Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information Security in Big Data: Privacy and Data Mining. IEEE Access, 2, 1149-1176. http://dx.doi.org/10.1109/access.2014.2362522