14 citations · 29 across the 5 of their papers we have counts for
6 papers
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…
Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing
Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé +4
Cyber Threat Intelligence (CTI) sharing is an important activity to reduce information asymmetries between attackers and defenders. However, this activity presents challenges due t…
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…
Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices
Ludovic Barman, Alexandre Dumur, Apostolos Pyrgelis +1
Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-…