Improving the Applicability of Differential Privacy in Data Sharing and Analytics Applications

Gonzalo Munilla Garrido

Improving the Applicability of Differential Privacy in Data Sharing and Analytics Applications

Gonzalo Munilla Garrido

Informatics 19 - Chair of Software Engineering for Business Information Systems

Research output: Types of Thesis › Doctoral Thesis

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
Supervisors/Advisors	Matthes, Florian, Supervisor
State	Submitted - 17 Apr 2023

Cite this

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title = "Improving the Applicability of Differential Privacy in Data Sharing and Analytics Applications",

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.",

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N2 - 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.

AB - 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.

M3 - Doctoral Thesis

ER -

Improving the Applicability of Differential Privacy in Data Sharing and Analytics Applications

Abstract

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