Bachelor's Thesis Jared Fantaye

An Introduction and Overview of Privacy-Enhancing Technologies for Data Processing and Analysis

Nowadays, data represents a valuable asset collected and analyzed by a wide range of companies in order to gain valuable business insights and create significant business value. While this may benefit businesses and consumers, it could also infringe on individuals' privacy rights. To counteract this kind of privacy invasion, Privacy-Enhancing Technologies (PETs) have been developed to allow organizations to work with sensitive information while still adhering to various data protection laws and regulations. However, several challenges still hinder the widespread adoption of PETs, such as their complexity, a lack of awareness, and the fact that current privacy laws do not mandate the use of these PETs. The combination of these factors has led to a lack of understanding and, therefore, a lack of adoption of Privacy-Enhancing Technologies by the industry.

To address this problem, this thesis aims to provide a comprehensive introduction and overview of the most predominant PETs for data processing and analysis. Its objective is to enable individuals from diverse backgrounds with no prior knowledge in this field to understand better the characteristics, benefits, challenges, and potential use cases of these PETs. To accomplish this, we first conducted a quantitative analysis to identify the five most prevalent PETs in current literature: Federated Learning, Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation, and Zero-Knowledge Proofs. Intending to obtain a fundamental understanding of each technology, we then conducted a Systematic Literature Review to find the most relevant publications from a large body of work. Based on the synthesized information and the help of several learning frameworks, such as Bloom's Revised Taxonomy and Gagné's Nine Events of Instruction, we could finally develop engaging and easy-to-understand learning content tailored to a non-technical readership. The resulting educational materials can provide a wide range of audiences with a basic understanding of the most prevalent PETs in the current literature.

Research Questions

This thesis seeks to answer the following research questions:

1. What are the most prevalent PETs for data processing and analysis?
2. What are the characteristics, benefits, challenges, and applications of the selected PETs?
3. How can one convey this information in a meaningful and engaging manner to ensure a better understanding of the selected PETs?