2 papers
cs.CR2024
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…
cs.CR2024
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…