Mike Mireku Kwakye is an assistant professor at the department of computer science at Fort Hays State University. His research interests are in data privacy, privacy-preserving models & methodologies, privacy-preserving databases, data science and analytics, secure information systems, and blockchains. He has attended and presented research works at several international conferences, seminars, and workshops. Moreover, he has over the years earned multiple awards and fellowships from his leading research works. He continues to do research with collaborators and students in both systems and applied research in ongoing themes in privacy, security, and data science.

Privacy preserving data preprocessing for web usage data mining Many online systems offer recommendations of varied aspects to web users to provide enough utility and the right kind of user preference data. Most of these recommendations arise out of well-processed underlying data from data mining procedures. The data mining procedures tend to expose seemingly private data item values to unintended analysts, especially that of demographic data. In this paper, the research study proposes a privacy-preserving framework architecture that enforces privacy on the data item values of web log and demographic data of users. Privacy issues are addressed by introducing a privacy-preserving database that stores a privacy commitment tuple; composed of the data item value and the privacy-preservation property values. This methodology approach proposes to offer merits in ensuring the protection of web users’ privacy information during data mining tasks. Additionally, the approach offers different levels of protection; where on the user-centric level, users’ personal privacy preferences are enabled, as part of subscribing to the laid down privacy policy on the web system. On the other hand, general privacy protection is enforced at the system-level by service providers (SPs).