10 citations · 12 across the 5 of their papers we have counts for
6 papers
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study
Natalija Mitic, Apostolos Pyrgelis, Sinem Sav
In this paper, we address the problem of privacy-preserving hyperparameter (HP) tuning for cross-silo federated learning (FL). We first perform a comprehensive measurement study th…
P3LI5: Practical and Confidential Lawful Interception on the 5G Core
Francesco Intoci, Julian Sturm, Daniel Fraunholz +2
Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain in…
slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference
Francesco Intoci, Sinem Sav, Apostolos Pyrgelis +3
Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Pred…
Scalable and Privacy-Preserving Federated Principal Component Analysis
David Froelicher, Hyunghoon Cho, Manaswitha Edupalli +6
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on priv…
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…
Privacy-Preserving Federated Recurrent Neural Networks
Sinem Sav, Abdulrahman Diaa, Apostolos Pyrgelis +2
We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relyin…