Abstract
This dissertation improves the applicability of privacy-enhancing technologies (PETs), particularly differential privacy (DP), in data sharing and analytics applications (DSAAs). Using literature reviews, expert interviews, and design science, it offers three contributions: (I) revealing challenges and opportunities of PETs' in DSAAs, (II) improving the verifiability of DP algorithms, and (III) proposing fundamental requirements for DP systems.
| Original language | American English |
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| State | Submitted - 17 Apr 2023 |