cid 8f8ff05bc3180eee673650f56c41864fbddc03967e5d3a913fed310132749588 · original · open access · via openalex
Title: Syft 0.5: A Platform for Universally Deployable Structured Transparency Authors: Adam James Hall; Madhava Jay; Tudor Cebere; Bogdan Cebere; Koen Lennart van der Veen; George Muraru; Tongye Xu; Patrick Cason; William J. Abramson; Ayoub Benaissa; Shah, Chinmay; Alan Aboudib; Théo Ryffel; Kritika Prakash; Tom Titcombe; Varun Kumar Khare; Maddie Shang; Ionesio Junior; Animesh Gupta; Jason Paumier; Nahua Kang; Vova Manannikov; Andrew Trask Venue: arXiv (Cornell University) Year: 2021 Canonical: doi:10.48550/arxiv.2104.12385 URL: https://doi.org/10.48550/arxiv.2104.12385 Collector: openalex OA: true ABSTRACT We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of a novel privacy-preserving inference information flow where we pass homomorphically encrypted activation signals through a split neural network for inference. We show that splitting the model further up the computation chain significantly reduces the computation time of inference and the payload size of activation signals at the cost of model secrecy. We evaluate our proposed flow with respect to its provision of the core structural transparency principles.