# Towards General-purpose Infrastructure for Protecting Scientific Data Under Study

arXiv: https://arxiv.org/abs/2110.01315 · HTML: https://mlsanity.com/paper/2110.01315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Subsampled R'enyi Differential Privacy and Analytical Moments Accountant](https://mlsanity.com/paper/1808.00087/) (2018)
- [Concentrated Differential Privacy](https://mlsanity.com/paper/1603.01887/) (2016)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Individual Privacy Accounting via a Renyi Filter](https://mlsanity.com/paper/2008.11193/) (2020)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Towards Practical Differential Privacy for SQL Queries](https://mlsanity.com/paper/1706.09479/) (2017)
- [Private Machine Learning in TensorFlow using Secure Computation](https://mlsanity.com/paper/1810.08130/) (2018)
- [Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy](https://mlsanity.com/paper/1407.6845/) (2014)
- [A Programming Framework for Differential Privacy with Accuracy Concentration Bounds](https://mlsanity.com/paper/1909.07918/) (2019)
- [Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy](https://mlsanity.com/paper/1909.02481/) (2019)
- [Fuzzi: A Three-Level Logic for Differential Privacy](https://mlsanity.com/paper/1905.12594/) (2019)

## Abstract (arXiv)

The scientific method presents a key challenge to privacy because it requires many samples to support a claim. When samples are commercially valuable or privacy-sensitive enough, their owners have strong reasons to avoid releasing them for scientific study. Privacy techniques seek to mitigate this tension by enforcing limits on one's ability to use studied samples for secondary purposes. Recent work has begun combining these techniques into end-to-end systems for protecting data. In this work, we assemble the first such combination which is sufficient for a privacy-layman to use familiar tools to experiment over private data while the infrastructure automatically prohibits privacy leakage. We support this theoretical system with a prototype within the Syft privacy platform using the PyTorch framework.