Dealing with highly sensitive data, identity management systems must provide adequate privacy protection as they leverage biometrics technology. Wielding Multi-Party Computation (MPC), Homomorphic Encryption (HE) and Functional Encryption (FE), this thesis tackles the design and implementation of practical privacy-preserving biometric systems, from the feature extraction to the matching with enrolled users.
This work is consecrated to the design of secure biometric solutions for multiple scenarios, putting special care to balance accuracy and performance with the security guarantees, while improving upon existing works in the domain. We go beyond privacy preservation against semi-honest adversaries by also ensuring correctness facing malicious adversaries. Lastly, we address the leakage of biometric data when revealing the output, a privacy concern often overlooked in the literature. The main contributions of this thesis are:
• A new face identification solution built on FE-based private inner product matching mitigating input leakage.
• A novel efficient two-party computation protocol, Funshade, to preserve the privacy of biometric thresholded distance metric operations.
• An innovative method to perform privacy-preserving biometric identification based on the notion of group testing named Grote.
• A new distributed decryption protocol with collaborative masking addressing input leakage, dubbed Colmade.
• An honest majority three-party computation protocol, Banners, to perform maliciously secure inference of Binarized Neural Networks.
• A HE Python library named Pyfhel, offering a high-level abstraction and low-level functionalities, with applications in teaching.
