6 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…
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…
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…
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,…
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…
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…