# Syft 0.5: A Platform for Universally Deployable Structured Transparency

arXiv: https://arxiv.org/abs/2104.12385 · HTML: https://mlsanity.com/paper/2104.12385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [No Peek: A Survey of private distributed deep learning](https://mlsanity.com/paper/1812.03288/) (2018)
- [Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning](https://mlsanity.com/paper/2011.09350/) (2020)
- [A Distributed Trust Framework for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2006.02456/) (2020)
- [Privacy preserving Neural Network Inference on Encrypted Data with GPUs](https://mlsanity.com/paper/1911.11377/) (2019)

## Built on by (most cited first, 3 of 3)

- [Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2202.13351/) (2022)
- [An automatic differentiation system for the age of differential privacy](https://mlsanity.com/paper/2109.10573/) (2021)
- [FedKit: Enabling Cross-Platform Federated Learning for Android and iOS](https://mlsanity.com/paper/2402.10464/) (2024)

## Abstract (arXiv)

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.