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End-to-End Encrypted Collaborative Documents
Collaborative documents (e.g., Google Docs, Microsoft 365) often contain sensitive information such as personal or financial data. In …
Christian Knabenhans, Zayd Maradni, Carmela Troncoso
USENIX Sec'26 Preprint Soon™️ Code
Humanitarian Aid Distribution with Privacy-Preserving Assessment Capabilities
In times of crisis, humanitarian organizations bring aid to those affected (e.g., water, food, medical supplies, cash assistance). …
Christian Knabenhans, Lucy Qin, Justinas Sukaitis, Vincent Graf Narbel, Carmela Troncoso
PETS'26 Preprint Soon™️ Code
On the Fiat–Shamir Security of Succinct Arguments from Functional Commitments
We study the security of a popular paradigm for constructing SNARGs, closing a key security gap left open by prior work. The paradigm …
Alessandro Chiesa, Ziyi Guan, Christian Knabenhans, Zihan Yu
ePrint
Lova: Lattice-Based Folding Scheme from Unstructured Lattices
Folding schemes (Kothapalli et al., CRYPTO 2022) are a conceptually simple, yet powerful cryptographic primitive that can be used as a …
Giacomo Fenzi, Duc Tu Pham, Christian Knabenhans, Ngoc Khanh Nguyen
Asiacrypt'24 ePrint Code Blog
VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption
Providing integrity guarantees for Fully Homomorphic Encryption using Homomorphic Message Authentication Codes.
Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis, Carmela Troncoso, Jean-Pierre Hubaux
CCS'24 arXiv Code
VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption
vFHE: Verifiable Fully Homomorphic Encryption
Fully Homomorphic Encryption (FHE) is a powerful building block for secure and private applications. However, state-of-the-art FHE …
Christian Knabenhans, Alexander Viand, Antonio Merino-Gallardo, Anwar Hithnawi
WAHC'24 PDF Code
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning
Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data …
Hidde Lycklama, Alexander Viand, Nicolas Küchler, Christian Knabenhans, Anwar Hithnawi
USENIX Sec'24 arXiv Code
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning