Publications by Apostolos Pyrgelis

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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 privacy and integrity concerns. Consequently, cautious users may want to prot ...

Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed ...

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 insightful data from network communications for criminal proceedings, e.g., ne ...

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

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 private data distributed among multiple data providers while ensuring data confi ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

Organizational networks are vulnerable to traffic-analysis attacks that enable adversaries to infer sensitive information from the network traffic – even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are p ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggrega ...

Apostolos Pyrgelis

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Privacy and Integrity Preserving Computations with CRISP

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Secure and federated genome-wide association studies for biobank-scale datasets

Scalable and Privacy-Preserving Federated Principal Component Analysis

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Deploying Decentralized, Privacy-Preserving Proximity Tracing

PriFi: Low-Latency Anonymity for Organizational Networks

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

Measuring Membership Privacy on Aggregate Location Time-Series

Scalable Privacy-Preserving Distributed Learning