6 papers
Eliminating Exponential Key Growth in PRG-Based Distributed Point Functions
Marc Damie, Florian Hahn, Andreas Peter +1
Distributed Point Functions (DPFs) enable sharing secret point functions across multiple parties, supporting privacy-preserving technologies such as Private Information Retrieval,…
Energy Consumption of TLS, Searchable Encryption and Fully Homomorphic Encryption
Marc Damie, Mihai Pop, Merijn Posthuma
Privacy-enhancing technologies (PETs) have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data pro…
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
Marc Damie, Florian Hahn, Andreas Peter +1
Ishai et al. (FOCS'06) introduced secure shuffling as an efficient building block for private data aggregation. Recently, the field of differential privacy has revived interest in…
Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
Marc Damie, Edwige Cyffers
Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer m…
Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption
Marc Damie, Jean-Benoist Leger, Florian Hahn +1
Encrypted search schemes have been proposed to address growing privacy concerns. However, several leakage-abuse attacks have highlighted some security vulnerabilities. Recent attac…
Evaluating Membership Inference Attacks in heterogeneous-data setups
Bram van Dartel, Marc Damie, Florian Hahn
Among all privacy attacks against Machine Learning (ML), membership inference attacks (MIA) attracted the most attention. In these attacks, the attacker is given an ML model and a…