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Master theses

Current and past ideas and concepts for Master Theses.

Social Media Analytics and Privacy


Nowadays people can easily access a substantial amount of information via social media. From Web 2.0, the Internet users are able to post their personal data online. User-generated content helps people customize their preferences, which are collected by service providers to improve their services. However, improper use of personal data has been reported. In Mar 2018, a company in Cambride, UK has been accused for the illegal usage of Facebook user data in supporting the US election in 2016. Facebook suffered from this incident when plenty of their users deactivate their accounts following the trend #DeleteFacebook. Recently, the European parliament has approved the General Data Protection Regulation (GDPR), which strictly control the use of personal data. Undoubtedly, data protection and privacy have become very important for every organization.
Nevertheless, there is always a trade-off between utility and privacy. Limiting one aspect will definitely affect the other. For this reason, ensuring utility but still protecting privacy is of great significance.

Kind of work

The objective of this thesis is to explore and develop methods for manipulating data such that on one hand specific utility is provided and on the other hand privacy is protected. Students are going to design or learn functions that allow supporting one task while hiding the information for the other tasks.

Framework of the Thesis

Such functions might be integrated directly at a data input scheme or utilized to sanitize data. Later, the sanitized data might be published for scientific purpose.
Students can use some publicly available datasets. Another option is to collect user profiles from open data platforms such as Twitter.

Number of Students


Expected Student Profile

- Computer science, applied computer science, engineering
- Good knowledge in machine learning and mathematics
- Good programming skill (Python, C++, Matlab)
- Interested in social network analytics


Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)2 629 1683

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Mr. Tien Do

+32 (0)2 629 2930

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