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Input Secrecy & Output Privacy: Efficient Secure Computation of Differential Privacy Mechanisms

Böhler, Jonas

Abstract (englisch):

Data is the driving force of modern businesses. For example, customer-generated data is collected by companies to improve their products, discover emerging trends, and provide insights to marketers. However, data might contain personal information which allows to identify a person and violate their privacy. Examples of privacy violations are abundant – such as revealing typical whereabout and habits, financial status, or health information, either directly or indirectly by linking the data to other available data sources. To protect personal data and regulate its collection and processing, the general data protection regulation (GDPR) was adopted by all members of the European Union.

Anonymization addresses such regulations and alleviates privacy concerns by altering personal data to hinder identification. Differential privacy (DP), a rigorous privacy notion for anonymization mechanisms, is widely deployed in the industry, e.g., by Google, Apple, and Microsoft.
Additionally, cryptographic tools, namely, secure multi-party computation (MPC), protect the data during processing. MPC allows distributed parties to jointly compute a function over their data such that only the function output is revealed but none of the input data. ... mehr

Volltext §
DOI: 10.5445/IR/1000141098
Veröffentlicht am 16.12.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Hochschulschrift
Publikationsdatum 16.12.2021
Sprache Englisch
Identifikator KITopen-ID: 1000141098
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xi, 134 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Theoretische Informatik (ITI)
Prüfungsdatum 10.11.2021
Schlagwörter secure multi-party computation, differential privacy, cryptography, anonymization
Referent/Betreuer Müller-Quade, J.
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