4 citations · 8 across the 9 of their papers we have counts for
11 papers
DDH-based schemes for multi-party Function Secret Sharing
Marc Damie, Florian Hahn, Andreas Peter +1
Function Secret Sharing (FSS) schemes enable sharing efficiently secret functions. Schemes dedicated to point functions, referred to as Distributed Point Functions (DPFs), are the…
TOSSS: a CVE-based Software Security Benchmark for Large Language Models
Marc Damie, Murat Bilgehan Ertan, Domenico Essoussi +3
With their increasing capabilities, Large Language Models (LLMs) are now used across many industries. They have become useful tools for software engineers and support a wide range…
Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
Marc Damie, Florian Hahn, Andreas Peter +1
To preserve data privacy, multi-party computation (MPC) enables executing Machine Learning (ML) algorithms on private data. However, MPC frameworks do not include optimized operati…
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…
Passive Query-Recovery Attack Against Secure Conjunctive Keyword Search Schemes
Marco Dijkslag, Marc Damie, Florian Hahn +1
While storing documents on the cloud can be attractive, the question remains whether cloud providers can be trusted with storing private documents. Even if trusted, data breaches a…
A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents
Marc Damie, Florian Hahn, Andreas Peter
Cloud data storage solutions offer customers cost-effective and reduced data management. While attractive, data security issues remain to be a core concern. Traditional encryption…