Mar 6, 2024 · This work examines the past and present of PPML, focusing on Homomorphic Encryption (HE) and Secure Multi-party Computation (SMPC) applied to ML.
The main aim of this Systemization of Knowledge (SoK) paper is to survey and compare State-of-the-Art (SotA) works in the area of HE and SMPC-based PPML in the ...
Mar 6, 2024 · The main techniques used to achieve PPML are: Homomorphic Encryption (HE) [61], Se- cure Multi-party Computation (SMPC) [158] , Federated ...
SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training (PETS 2024). Sven Bugiel. SoK: Lessons Learned From Android Security ...
Mar 7, 2024 · Title: Wildest Dreams: Reproducible Research in Privacy-Preserving Neural Network Training Authors:Tanveer Khan, Mindaugas Budzys, ...
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This work reviews the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and ...
Tanveer Khan, Mindaugas Budzys , Khoa Nguyen, Antonis Michalas: SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training.
SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training [PDF] Tanveer Khan (Tampere University), Mindaugas Budzys (Tampere ...
In addition, we present a SoK of the most recent PPML frameworks for model training and provide a comprehensive comparison in terms of the unique properties and ...
SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training, Tanveer Khan, Mindaugas Budzys, Khoa Nguyen, Antonis Michalas. 2023.