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Master Thesis - Identification and Evaluation of Approaches to Secure Big Data Analytics in Cloud Environments Using De-Identification Methods

Last modified Jul 30
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Motivation and Goal

Big Data Analytics rely on cloud infrastructures, which are also a central point of attack for adverseries. To ensure data security in cloud environments, a solution based on a security gateway is envisioned. The security gateway will use de-identification techniques to ensure that sensitive data is not stored in plain text in the cloud while at the same time enabling as many analytics functions as possible. In addition, the client can access multiple distributed data processing applications through a single API. 

The Thesis should answer the following research questions:

  • RQ1: What are the current approaches for secure big data analytics in cloud environments and what are their advantages and disadvantages?
  • RQ2: What are the requirements for Big Data Analytics in the Cloud (in Germany)?
  • RQ3: What are concepts enabling secure big data analytics in the cloud based on a security gateway and de-identification methods?


Industry Partner

This project is conducted in cooperation with an industry partner.



  • Experiences with regards to distributed big data processing (e.g. Hadoop, Spark)
  • Master student of Informatics, Information Systems, or Management and Technology (TUM-BWL)
  • Fluent in English


Type of thesis

  • Master Thesis, Guided Research, Hiwi position
  • This Thesis comprises conceptual work as well as the implementation of a prototype



Please send your (1) CV and (2) Transcript of records to 


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