15 citations · 26 across the 3 of their papers we have counts for
3 papers
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning
Hidde Lycklama, Alexander Viand, Nicolas Küchler +2
Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due t…
Verifiable Fully Homomorphic Encryption
Alexander Viand, Christian Knabenhans, Anwar Hithnawi
Fully Homomorphic Encryption (FHE) is seeing increasing real-world deployment to protect data in use by allowing computation over encrypted data. However, the same malleability tha…
Verifiable Encodings for Secure Homomorphic Analytics
Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis +2
Homomorphic encryption, which enables the execution of arithmetic operations directly on ciphertexts, is a promising solution for protecting privacy of cloud-delegated computations…