4 papers
Quantization Enables Private Dense Retrieval against Malicious Service Providers
Louis Tremblay Thibault, Sofiane Azogagh, Marc-Olivier Killijian +1
Dense retrieval, the key component of Retrieval Augmented Generation (RAG), retrieves the most relevant documents by comparing dense vector representations of queries and passages…
Manipulation-Proof Oblivious Audits against Deceptive Model Providers
Augustin Godinot, Sofiane Azogagh, Julien Ferry +1
Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models. However, ensuring th…
GRAND : Graph Reconstruction from potential partial Adjacency and Neighborhood Data
Sofiane Azogagh, Zelma Aubin Birba, Josée Desharnais +3
Cryptographic approaches, such as secure multiparty computation, can be used to compute in a secure manner the function of a distributed graph without centralizing the data of each…
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning
Sofiane Azogagh, Zelma Aubin Birba, Sébastien Gambs +1
Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is parti…