5 citations · 16 across the 9 of their papers we have counts for
13 papers · 1 filter
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…
SoK: Privacy-Preserving Collaborative Tree-based Model Learning
Sylvain Chatel, Apostolos Pyrgelis, Juan Ramon Troncoso-Pastoriza +1
Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal n…
Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies
Anisa Halimi, Leonard Dervishi, Erman Ayday +5
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed…
Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis
James Scheibner, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza +4
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usa…
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +4
In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an -party, federated learning setting. We propose a novel system, POSEI…
Privacy and Integrity Preserving Computations with CRISP
Sylvain Chatel, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +1
In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise p…
Scalable Privacy-Preserving Distributed Learning
David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis +4
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstracti…
Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets
David Froelicher, Juan R. Troncoso-Pastoriza, Joao Sa Sousa +1
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensiti…
Multivariate Cryptosystems for Secure Processing of Multidimensional Signals
Alberto Pedrouzo-Ulloa, Juan Ramón Troncoso-Pastoriza, Fernando Pérez-González
Multidimensional signals like 2-D and 3-D images or videos are inherently sensitive signals which require privacy-preserving solutions when processed in untrustworthy environments,…
On Ring Learning with Errors over the Tensor Product of Number Fields
Alberto Pedrouzo-Ulloa, Juan Ramón Troncoso-Pastoriza, Fernando Pérez-González
The "Ring Learning with Errors" (RLWE) problem was formulated as a variant of the "Learning with Errors" (LWE) problem, with the purpose of taking advantage of an additional algebr…
Number Theoretic Transforms for Secure Signal Processing
Alberto Pedrouzo-Ulloa, Juan Ramón Troncoso-Pastoriza, Fernando Pérez-González
Multimedia contents are inherently sensitive signals that must be protected whenever they are outsourced to an untrusted environment. This problem becomes a challenge when the untr…