Privacy Preserving Fingerprint Matching

Julia Mader

Privacy Preserving Fingerprint Matching

Julia Mader

Research output: Thesis › Master's Thesis

Abstract

This thesis focuses on implementing and optimizing a minutiae-based fingerprint matching algorithm with Multiparty Computation (MPC), addressing the challenge of maintaining privacy while ensuring reasonable computation times. The author presents an implementation using the minutiae-based matcher SourceAFIS, optimized for MPC, and evaluates its performance to assess if current protocols are ready for deployment in time critical scenarios. Results show promise, emphasizing our ongoing research to achieve a fully-fledged MPC implementation with high accuracy.

Original language	English
Qualification	Master of Science
Awarding Institution	Technical University of Munich (TUM)
Supervisors/Advisors	Lorünser, Thomas, Supervisor
Award date	12 Sept 2023
Publication status	Published - 12 Sept 2023

Research Field

Cyber Security

Keywords

Multiparty Computation
Fingerprint Matching
Privacy Preservation

Cite this

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title = "Privacy Preserving Fingerprint Matching",

abstract = "This thesis focuses on implementing and optimizing a minutiae-based fingerprint matching algorithm with Multiparty Computation (MPC), addressing the challenge of maintaining privacy while ensuring reasonable computation times. The author presents an implementation using the minutiae-based matcher SourceAFIS, optimized for MPC, and evaluates its performance to assess if current protocols are ready for deployment in time critical scenarios. Results show promise, emphasizing our ongoing research to achieve a fully-fledged MPC implementation with high accuracy.",

keywords = "Multiparty Computation, Fingerprint Matching, Verschl{\"u}sselung, Kryptographie, Multiparty Computation, Fingerprint Matching, Privacy Preservation",

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school = "Technical University of Munich (TUM)",

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AU - Mader, Julia

PY - 2023/9/12

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N2 - This thesis focuses on implementing and optimizing a minutiae-based fingerprint matching algorithm with Multiparty Computation (MPC), addressing the challenge of maintaining privacy while ensuring reasonable computation times. The author presents an implementation using the minutiae-based matcher SourceAFIS, optimized for MPC, and evaluates its performance to assess if current protocols are ready for deployment in time critical scenarios. Results show promise, emphasizing our ongoing research to achieve a fully-fledged MPC implementation with high accuracy.

AB - This thesis focuses on implementing and optimizing a minutiae-based fingerprint matching algorithm with Multiparty Computation (MPC), addressing the challenge of maintaining privacy while ensuring reasonable computation times. The author presents an implementation using the minutiae-based matcher SourceAFIS, optimized for MPC, and evaluates its performance to assess if current protocols are ready for deployment in time critical scenarios. Results show promise, emphasizing our ongoing research to achieve a fully-fledged MPC implementation with high accuracy.

KW - Multiparty Computation

KW - Fingerprint Matching

KW - Verschlüsselung

KW - Kryptographie

KW - Multiparty Computation

KW - Fingerprint Matching

KW - Privacy Preservation

M3 - Master's Thesis

ER -